Evolutionary Principles Research Articles

A Nexus between Genetic and Non-Genetic Mechanisms Guides KRAS Inhibitor Resistance in Lung Cancer.

Several studies in the last few years have determined that, in contrast to the prevailing dogma that drug resistance is simply due to Darwinian evolution-the selection of mutant clones in response to drug treatment-non-genetic changes can also lead to drug resistance whereby tolerant, reversible phenotypes are eventually relinquished by resistant, irreversible phenotypes. Here, using KRAS as a paradigm, we illustrate how this nexus between genetic and non-genetic mechanisms enables cancer cells to evade the harmful effects of drug treatment. We discuss how the conformational dynamics of the KRAS molecule, that includes intrinsically disordered regions, is influenced by the binding of the targeted therapies contributing to conformational noise and how this noise impacts the interaction of KRAS with partner proteins to rewire the protein interaction network. Thus, in response to drug treatment, reversible drug-tolerant phenotypes emerge via non-genetic mechanisms that eventually enable the emergence of irreversible resistant clones via genetic mutations. Furthermore, we also discuss the recent data demonstrating how combination therapy can help alleviate KRAS drug resistance in lung cancer, and how new treatment strategies based on evolutionary principles may help minimize or even preclude the emergence of drug resistance.

An investigation into the morphological distribution of the temperature field in loose media and its underlying principles of evolution

The spontaneous combustion of coal in goaf has important implications for the safe mining of working faces; hence, it is crucial to prevent and control such occurrences. Here, we establish an experimental system to investigate the morphological distribution, evolution, and migration process of the temperature field of the thermal core area in systems with various particle sizes. Furthermore, we propose a high-temperature-region inversion method based on the morphological evolution model of the temperature field. Our experimental results demonstrate that particle size is a critical factor influencing the temperature field morphology under a given heating condition. During the heating stage, the isothermal surface within the thermal core area forms an ellipsoidal shape, with a transverse section comprising concentric circles, and a longitudinal section comprising ellipses. During the heat dissipation stage, the isothermal surface of the small-particle-size system encircles the thermal core area from the upper direction downwards, while the isothermal surface of the large-particle-size system encircles the thermal core area from the lower direction upwards. Moreover, the thermal core area of the large-particle-size system migrates upwards, while the thermal core area of the small-particle-size system migrates a shorter distance. Finally, we used a geometrically simplified temperature field in loose media to propose a high-temperature-region inversion method, which was verified by experiments. This study is crucial for understanding the distribution and evolution of temperature fields in loose media and predicting the formation and development of high-temperature regions.

Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters.

Structural characterization of nanoclusters is one of the major challenges in nanocluster modeling owing to the multitude of possible configurations of arrangement of cluster atoms. The genetic algorithm (GA), a class of evolutionary algorithms based on the principles of natural evolution, is a commonly employed search method for locating the global minimum configuration of nanoclusters. Although a GA search at the DFT level is required for the accurate description of a potential energy surface to arrive at the correct global minimum configuration of nanoclusters, computationally expensive DFT evaluation of the significantly larger number of cluster geometries limits its practicability. Recently, machine learning potentials (MLP) that are learned from DFT calculations gained significant attention as computationally cheap alternative options that provide DFT level accuracy. As the accuracy of the MLP predictions is dependent on the quality and quantity of the training DFT data, active learning (AL) strategies have gained significant momentum to bypass the need of large and representative training data. In this application note, we present Cluster-MLP, an on-the-fly active learning genetic algorithm framework that employs the Flare++ machine learning potential (MLP) for accelerating the GA search for global minima of pure and alloyed nanoclusters. We have used a modified version the Birmingham parallel genetic algorithm (BPGA) for the nanocluster GA search which is then incorporated into distributed evolutionary algorithms in Python (DEAP), an evolutionary computational framework for fast prototyping or technical experiments. We have shown that the incorporation of the AL framework in the BPGA significantly reduced the computationally expensive DFT calculations. Moreover, we have shown that both the AL-GA and DFT-GA predict the same global minima for all the clusters we tested.

Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine

BackgroundProstate cancer (PrCa) genomic heterogeneity causes resistance to therapies such as androgen deprivation. Such heterogeneity can be deciphered in the context of evolutionary principles, but current clinical trials do not include evolution as an essential feature. Whether or not analysis of genomic data in an evolutionary context in primary prostate cancer can provide unique added value in the research and clinical domains remains an open question.MethodsWe used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high-risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.ResultsIn patient GP5, CDK12 inactivation was among the first mutations, leading to a PrCa tandem duplicator phenotype and initiating the cancer around age 50, followed by rapid cancer evolution after age 57, and metastasis around age 59, 5 years prior to prostatectomy. In patient GP12, accelerated cancer progression was detected after age 54, and metastasis occurred around age 56, 3 years prior to prostatectomy. Multiple metastasis-originating events were identified in each patient and tracked anatomically. Metastasis from prostate to lymph nodes occurred strictly ipsilaterally in all 12 detected events. In this pilot, metastatic subclone content analysis appears to substantially enhance the identification of key drivers. Evolutionary analysis’ potential impact on therapy selection appears positive in these pilot cases.ConclusionsPrCa evolutionary analysis allows tracking of anatomic site of origin, timing of cancer origin and spread, and distinction of metastatic-capable from non-metastatic subclones. This enables better identification of actionable targets for therapy. If extended to larger cohorts, it appears likely that similar analyses could add substantial biological insight and clinically relevant value.

Differentially Private Simple Genetic Algorithms

The differentially private (DP) selection problem is a fundamental building block in the private literature that is commonly solved with the exponential mechanism. It is well known that efficiency is the major drawback of the exponential mechanism, as the utility function must be computed for all elements in the domain. Genetic algorithms (GAs) use the principles of evolution in nature to efficiently search through large domains and select the best candidate. We observe that GAs have many appealing properties for DP Selection. These include being robust to noisy objectives, placing no restriction on the utility function, and efficient runtime for large domains. However, prior work investigating DP GAs has shown poor utility in practice and often gives the highest utility when zero generations are conducted (indicating that GA operations are not beneficial under DP). This work provides a new DPGA based on the simple GA that addresses the weaknesses of prior solutions. We reduce the destructive nature of previous GA operators and utilize several techniques to reduce the noise from DP. Our modifications allow us to utilize the GA operators over multiple generations (under DP) and improve the GA's overall utility over zero generation techniques. Our work shows that private GAs are competitive with state-of-the-art general and problem-specific solutions to the DP selection problem, with runtime sublinear in the domain size.

Evolution of cephalopod nervous systems

Giant brains have independently evolved twice on this planet, in vertebrates and in cephalopods (Figure 1A). Thus, the brains and nervous systems of cephalopods provide an important counterpoint to vertebrates in the search for generalities of brain organization and function. Their mere existence disproves various hypotheses proposed to explain the evolution of the mind and the human brain, such as cognition and large brains evolved only in long-lived animals with complex social systems and parental care, none of which is true of cephalopods. Therefore, it is worthwhile to review what is known about the evolution of cephalopod nervous systems to consider how it informs our understanding of general principles of brain evolution.

ПРОБЛЕМА БИОЛОГИЧЕСКОГО И СОЦИАЛЬНОГО В ОТЕЧЕСТВЕННОЙ ФИЛОСОФИИ ВТОРОЙ ПОЛОВИНЫ XX — НАЧАЛА XXI ВЕКА: ИСТОРИКО-ФИЛОСОФСКИЙ АНАЛИТИЧЕСКИЙ ОБЗОР

The article shows a historical retrospective of ideas in the domestic philosophical thought of the second half of the 20th century — the beginning of the 21st century, aff ecting the problem of the interaction of biological and social aspects of human development, as well as epistemological and ontological reasons for their appearance. The fi rst historical and philosophical analysis of the problem of the relationship between the biological and the social in the works of Russian thinkers of the second half of the 20th century — the beginning of the 21st century is presented. As part of the analytical review, modern scientifi c publications were tested for the fi rst time in their historical relationship with previous scientifi c research. All this is the exceptional value and novelty of this scientific research. As a result of the analysis, it can be assumed that the presented problems were noted in the works of domestic researchers and, perhaps, their fi rst thoughts arose even earlier than those of foreign scientists; in domestic thought, the problem of interaction between biological and social was interpreted as “coevolution”, and in the works of foreign researchers it was designated as “the theory of gene-cultural evolution”. Consideration of the problem made it possible to reveal that the hypothesis of gene-cultural relationship was the initial stage in the formation and development of such a scientifi c and academic direction as sociobiology, and which is determined by its kind of inherent immanence. All this also determines the exceptional value and novelty of the presented scientific analysis. The study of the laws of heredity and the basic principles of evolution of both humanity and all animal and plant organisms of the ecosystem actualizes the importance of developing a methodology with which it becomes possible to know the natural mechanisms of interdependence and complementarity that are happening around us, to comprehend the technology of the emergence and development of life from the position of philosophy.

Motif-to-Core Nucleation in a Decahedral Evolution Pattern.

The atomic precision of ultrasmall metal nanoclusters has opened the door to elucidating the structural evolution principles of metal nanomaterials at the molecular level. Here, we report a novel set of super-atomic Ag clusters, including [Ag19(TBBT)16(DPPP)4]+ (Ag19), [Ag22(DMAT)8(DPPM)4Cl8]2+ (Ag22), Ag26(SPh3,5-CF3)15(DPPF)4Cl5 (Ag26), and [Ag30(DMAT)12(DPPP)4Cl8]2+ (Ag30). The core structures of these clusters correspond to one decahedral Ag7, perpendicular bi-decahedrons, three-dimensional penta-decahedrons, and hexa-decahedrons, respectively. The Ag atoms in AgS2 blocks show a strong correlation with the decahedral cores: the five equatorial Ag atoms in the decahedral Ag7 core of Ag19 all adopt the AgS2 coordination, while the Ag atoms in AgS2 blocks of Ag22, Ag26, and Ag30 unexceptionally constitute additional decahedral structures with the core Ag atoms. Specifically, two and four core Ag atoms of Ag26 and Ag30 clusters occupy positions that highly resemble that of Ag (in AgS2 motifs) of Ag22. The strong structural correlation demonstrates the motif-to-core evolution of the surface Ag (on AgS2) to build extra-decahedral blocks. Density functional theory calculations indicate that the 2e, 4e, 6e, and 8e clusters (from Ag19 to Ag30) adopt 1S2, 1S21P2, 1S21P4, and 1S21P6 electron configurations, all of which feature excellent super-atomic characters.

A population-based approach for multi-agent interpretable reinforcement learning

Multi-Agent Reinforcement Learning (MARL) made significant progress in the last decade, mainly thanks to the major developments in the field of Deep Neural Networks (DNNs). However, DNNs suffer from a fundamental issue: their lack of interpretability. While this is true for most applications of DNNs, this is exacerbated in their applications in MARL. In fact, the mutual interactions between agents and environment, as well as across agents, make it particularly difficult to understand learned strategies in these settings. One possible way to achieve explainability in MARL is through the use of interpretable models, such as decision trees, that allow for a direct inspection and understanding of their inner workings. In this work, we make a step forward in this direction, proposing a population-based algorithm that combines evolutionary principles with RL for training interpretable models in multi-agent systems. We evaluate the proposed approach in a highly dynamic task where two teams of agents compete with each other. We test different variants of the proposed method in different settings, namely with/without coevolution and with/without initialization from a handcrafted policy. We find that, in most settings, our method is able to find fairly effective policies. Moreover, we show that the learned policies are easy to inspect and, possibly, interpreted based on domain knowledge.

Multi-objective Evolutionary Algorithm Supported Construction of Architectural Window Design Model Based on Visual Comfort - A Case Study of Office Buildings in Cold Regions

Abstract With the accelerated advancement of big data Internet development, people gradually realize the urgency and importance of energy saving, so the multi-objective optimization research for the performance of office buildings in cold regions is significant, and this paper focuses on the construction of a building window design model based on visual comfort supported by multi-objective evolutionary algorithm- taking office buildings in cold regions as an example, firstly, by reviewing the literature to understand the principle, algorithm, and concept of multi-objective evolution, construct the building window design model based on visual comfort according to the objective function and extract the standard model of inner corridor slab space building in office buildings in cold regions of Harbin and Shenyang. The optimized design of building window parameters was carried out using the established joint simulation and optimization work platform. The results show that for Harbin, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. An increase in window heat transfer coefficient by 0.1W/ (m 2 ∙K) Increases are cooling energy consumption by 0.18%, heating energy consumption by 0.78%, and total energy consumption by 0.46%. For Shenyang, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. If the heat transfer coefficient of the window increases by 0.1W/ (m 2 ∙K), cooling energy consumption increases by 0.18%, heating energy consumption increases by 0.78%, and total energy consumption increases by 0.46%. This study provides a theoretical basis for extracting standard models for other building types while making the results more generalizable and improving the efficiency of sustainable office building design.

O03 The effect of antibiotic selection on collateral susceptibility and evolvability of uropathogenic Escherichia coli

Abstract Urinary tract infections (UTIs) are a common infection in healthy adult women and as such account for 1%–3% of all medical consultations and the second most common reason for prescribing antimicrobials in the UK. Escherichia coli is a frequent aetiological agent of uncomplicated UTIs, causing 75% of cases, associated with recurrence of infection (40%) and a major source of bacteraemia (∼40%). Trimethoprim and nitrofurantoin are currently recommended as first-line antibiotics for the treatment of uncomplicated UTIs. However, 48700 deaths in Europe in 2019 have been associated with antimicrobial-resistant UTIs and resistance to trimethoprim in community-acquired UTIs caused by E. coli is high (33.4%). With little to no antimicrobial development in recent years, new strategies are therefore required to maintain efficacy in currently available antibiotics. One such strategy is to use evolutionary principles to understand how antimicrobial resistance (AMR) to one antibiotic can affect the activity and probability of resistance development to a second antibiotic. Effect on antimicrobial activity can be assessed through collateral susceptibility, where prior resistance to one antibiotic either increases or decreases susceptibility to a second, unrelated class of antibiotic. While the probability of resistance development can be assessed by the mutant selection window (MSW), an antibiotic concentration range where AMR mutations can be selected for. This ranges from the MIC (the lowest concentration to select for resistance) to the mutant prevention concentration (MPC, the highest concentration to select for resistance). In this study, we aimed to determine the effect trimethoprim resistant derivatives of three clinical strains of uropathogenic E. coli on collateral susceptibility and MSWs to nitrofurantoin. We found that trimethoprim-resistant mutants conferred the expected decrease in susceptibility to trimethoprim and an increase in the MSW. This suggests prior selection with trimethoprim increases the probability of treatment failure and the selection of further mutations conferring high-level resistance to trimethoprim. We also observed collateral resistance to nitrofurantoin in the trimethoprim-resistant mutants. Importantly, however, there was a reduction in the MPC, and therefore a narrowing of the MSW, reducing the probability of selection of higher levels of nitrofurantoin resistance. This suggests that using nitrofurantoin after trimethoprim may reduce the probability of developing nitrofurantoin resistance compared with using nitrofurantoin first. These data provide a potential evidence base to use evolutionary principles when designing antimicrobial regimens to limit AMR development and improve treatment success.

Using the Modified Artificial Bee Colony Algorithm to Find the Non-Archimedean Epsilon for Evaluating the Efficiency in DEA

The artificial bee colony algorithm is one of the population-based optimization methods inspired by the evolutionary principles of the social behavior of bees. On the other hand, one of the sub-fields of operations research science is data envelopment analysis. There are some difficulties in DEA models for selecting the appropriate numerical value for an infinitesimal non-Archimedean epsilon. So far, various methods have been proposed to solve this problem and choose the suitable non-Archimedean epsilon. In order to solve the problem, the artificial bee colony algorithm (ABC), and modification of the original ABC algorithm (MABC) are adopted and proposed in this paper. The impacts of our proposed algorithms on the suitable non-Archimedean epsilon by solving only one linear programming (LP), instead of n LP are investigated. Finally, the performance of the proposed algorithms is evaluated by comparing the solutions obtained from GAMS software based on the presented examples.

Architecture for Self-Evolution of 6G Core Network Based on Intelligent Decision Making

The rapid progress of 6G mobile communication technologies has sparked a great deal research interests. The 6G core network architecture faces formidable challenges due to the escalating complexity of network service demands and diverse application scenarios. In response, our research endeavors to tackle these challenges by proposing a self-evolving architecture based on intelligent decision making. Inspired by the principles of biological morphological evolution, our architecture empowers the core network to dynamically adapt and reshape itself in order to effectively address the evolving communication environments. To facilitate this self-evolutionary process, we introduce a comprehensive framework encompassing mechanisms, architecture, agents, and algorithms that enable the network to autonomously generate and optimize its own structure, thereby ensuring adaptability to a wide range of application scenarios. By conducting concept proof simulation experiments, we have demonstrated the effectiveness of our self-evolution algorithm, which enables the 6G core network to make rational evolving decisions and exhibit remarkable adaptability to various application scenarios.

A brain-wide analysis maps structural evolution to distinct anatomical module.

The vertebrate brain is highly conserved topologically, but less is known about neuroanatomical variation between individual brain regions. Neuroanatomical variation at the regional level is hypothesized to provide functional expansion, building upon ancestral anatomy needed for basic functions. Classically, animal models used to study evolution have lacked tools for detailed anatomical analysis that are widely used in zebrafish and mice, presenting a barrier to studying brain evolution at fine scales. In this study, we sought to investigate the evolution of brain anatomy using a single species of fish consisting of divergent surface and cave morphs, that permits functional genetic testing of regional volume and shape across the entire brain. We generated a high-resolution brain atlas for the blind Mexican cavefish Astyanax mexicanus and coupled the atlas with automated computational tools to directly assess variability in brain region shape and volume across all populations. We measured the volume and shape of every grossly defined neuroanatomical region of the brain and assessed correlations between anatomical regions in surface fish, cavefish, and surface × cave F2 hybrids, whose phenotypes span the range of surface to cave. We find that dorsal regions of the brain are contracted, while ventral regions have expanded, with F2 hybrid data providing support for developmental constraint along the dorsal-ventral axis. Furthermore, these dorsal-ventral relationships in anatomical variation show similar patterns for both volume and shape, suggesting that the anatomical evolution captured by these two parameters could be driven by similar developmental mechanisms. Together, these data demonstrate that A. mexicanus is a powerful system for functionally determining basic principles of brain evolution and will permit testing how genes influence early patterning events to drive brain-wide anatomical evolution.

Modeling and Analyzing Layup Gaps in the Trajectory Planning for Automated Tape Placement

Composite materials are widely used in the aerospace industry thanks to their lightweight along with high stiffness and strength. Automated tape placement (ATP) is an important automated process used for the fabrication of large composite structures. In ATP, the layup paths of contiguous tapes are not parallel along their lengths due to the complex contour of the mandrel, which eventually introduces gaps or overlaps between the edges of tapes. Overlaps or excessive gaps are highly undesirable as they will weaken the resulting composite member. To date, little research reveals the generation and evolution principles of placement gaps on curved mold surfaces. A better understanding of the principles would help to adjust the layup trajectories to eliminate the deposition errors and ensure the layup quality. In this paper, the evolution law of layup gaps on mold surfaces is presented at first. Then, a placement gap model is established to predict the value of gaps in the trajectory planning stage. The gap model has been validated on a series of mold surfaces. One application of this model is to optimize the placement trajectories for ATP.

Influences of chemotype and parental genotype on metabolic fingerprints of tansy plants uncovered by predictive metabolomics

Intraspecific plant chemodiversity shapes plant-environment interactions. Within species, chemotypes can be defined according to variation in dominant specialised metabolites belonging to certain classes. Different ecological functions could be assigned to these distinct chemotypes. However, the roles of other metabolic variation and the parental origin (or genotype) of the chemotypes remain poorly explored. Here, we first compared the capacity of terpenoid profiles and metabolic fingerprints to distinguish five chemotypes of common tansy (Tanacetum vulgare) and depict metabolic differences. Metabolic fingerprints captured higher variation in metabolites while preserving the ability to define chemotypes. These differences might influence plant performance and interactions with the environment. Next, to characterise the influence of the maternal origin on chemodiversity, we performed variation partitioning and generalised linear modelling. Our findings revealed that maternal origin was a higher source of chemical variation than chemotype. Predictive metabolomics unveiled 184 markers predicting maternal origin with 89% accuracy. These markers included, among others, phenolics, whose functions in plant-environment interactions are well established. Hence, these findings place parental genotype at the forefront of intraspecific chemodiversity. We recommend considering this factor when comparing the ecology of various chemotypes. Additionally, the combined inclusion of inherited variation in main terpenoids and other metabolites in computational models may help connect chemodiversity and evolutionary principles.

Three Little Birds: Reassembling Typological Thought

The typologies that archaeologists use to classify artefacts and situate them chronologically and culturally are crucial tools of the discipline; when left unquestioned, however, they tend to produce reductive and essentializing understandings of the past. Like all theoretical interventions, assemblage theory questions the unquestioned, in this case, asking archaeologists to radically rethink the relationality of the world and the power and vibrancy of nonhuman and nonliving things like stone. In this article, we take an assemblage-based approach to an old typological problem – sorting birdstones. Since the mid-19th century, collectors and archaeologists categorized birdstones found throughout the American Northeast according to evolutionary or culture-historical principles. These approaches paid little attention to different varieties of stone, often regarding birdstones as if they were passive reflections of normative mindsets that came in only three culture-specific types. Here, we explore how archaeologists might ‘reassemble’ typological thought, analysing and thinking through a large sample of materially varied birdstones to find much more than three little birds. Recognizing how the shared and specific capacities of different stones actively contributed to the multiplicity of birdstone morphologies resituates them as singular and changing assemblages while highlighting the potentials of questioning the fixity of both typological and material categories at large.

Inheritance and inequality among nomads of South Siberia.

At the headwaters of the Yenisei River in Tuva and northern Mongolia, nomadic pastoralists move between camps in a seasonal rotation that facilitates their animals' access to high-quality grasses and shelter. The use and informal ownership of these camps depending on season helps illustrate evolutionary and ecological principles underlying variation in property relations. Given relatively stable patterns of precipitation and returns to capital improvement, families generally benefit from reusing the same camps year after year. We show that locations with higher economic defensibility and capital investment-winter camps and camps located in mountain/river valleys-are claimed and inherited more frequently than summer camps and camps located in open steppe. Camps are inherited patrilineally and matrilineally at a ratio of 2 : 1. Despite its practical importance, camp inheritance is not associated with livestock wealth today, which is better predicted by education and wealth outside the pastoral economy. The relationship between the livestock wealth of parents and their adult children is significantly positive, but relatively low compared to other pastoralists. The degree of inequality in livestock wealth, however, is very close to that of other pastoralists. This is understandable considering the durability and defensibility of animal wealth and economies of scale common across pastoralists. This article is part of the theme issue 'Evolutionary ecology of inequality'.

The principle underlying all evolution, biological, geophysical, social and technological.

This article addresses the main research areas identified in the call for contributions to this special issue. With examples from published articles and books, the present article shows that all the identified areas are already covered by the universal principle underlying all evolution: the constructal law (1996), i.e. the physics law of design evolution in nature (free morphing, flowing, moving systems). The universal principle of evolution belongs in thermodynamics because thermodynamics is a universal science and evolution is a universal phenomenon. The principle unites the natural sciences with the social sciences, and the living with the non-living. It unifies the world of science and its languages (energy, economy, evolution, sustainability, etc.), and brings together the natural and artificial flow architectures, the human made and the not human made. The principle establishes firmly in physics the reality that humans are part of nature. With the principle, physics extends its coverage over phenomena that were previously considered out of reach: social organization, economics and human perceptions. Such phenomena are physical, i.e. facts. The entire world depends on the science of useful things, and benefits greatly from a physics discipline with freedom, life, wealth, time, beauty and future. This article is part of the theme issue 'Thermodynamics 2.0: bridging the natural and social sciences (Part 1)'.

Rape with Murder and Suicide: The Evidential Argument from Evil against Naturalism

The problem of evil seems to have been the patent of theism for a long time. However, some philosophers notice that this is not necessarily the case and raise arguments from evil against atheism. In this paper, I follow this insight and raise the evidential argument from evil against naturalism. I argue that some human behaviors that cause evil and suffering contradict the principles of evolution and should not exist in a naturalistic world. Nevertheless, they do exist, and they accordingly disconfirm naturalism. To attain this conclusion, I first establish that psychological mechanisms as evolutionary causes are the ultimate causes of human behaviors if naturalism is true. Then, I argue that cases of rape with murder and suicide have contravened their relevant psychological mechanisms’ adaptive functions and should not exist. Therefore, cases of these behaviors make it reasonable to believe that naturalism is not true. Both naturalists and theists now have to raise plausible explanations for various evils in the world. It is possible for theism to outcompete naturalism with respect to evil as a result.