Evolutionary Programming Research Articles

ПРИМЕНЕНИЕ ГЕНЕТИЧЕСКИХ АЛГОРИТМОВ В КРИПТОГРАФИИ

Currently in the development of computer technologies that ensure information security and information protection, cryptographic methods of protection are widely used. The main tasks in cryptography are the development of new encryption features, difficult to break and repetitive ciphers. To solve that problem, falling into the class of NP-complete ones, algorithms based on natural principles have been used in recent years. These include genetic algorithms (GA), evolutionary methods, swarm intelligence algorithms. In models and algorithms of evolutionary computations, the construction of basic models and rules is implemented, according to which it can change (evolve). In recent years, evolutionary computing schemes have been proposed, including the genetic algorithm, genetic programming, evolutionary programming, and evolutionary strategies. The paper discusses the existing cryptography methods, basic concepts and methods of modern cryptography, the notion of a genetic algorithm, a universal hash function, as well as a hash detection method and a genetic hashing algorithm built on it. A genetic algorithm was implemented in the Golang language, modified for the current problem of finding the optimal hash functions. A detailed description of each stage of the algorithm execution is given. Also, within the framework of the research, a study of the function of the genetic algorithm itself and the genetic hashing algorithm was carried out, evaluating the convergence of the genetic algorithm depending on the input data, and evaluating the possible direction of further research.

MDA-TOEPGA: A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

The association between miRNA and disease has attracted more and more attention. Until now, existing methods for identifying miRNA related disease mainly rely on top-ranked association model, which may not provide a full landscape of association between miRNA and disease. Hence there is strong need of new computational method to identify the associations from miRNA group view. In this paper, we proposed a framework, MDA-TOEPGA, to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm, which identifies latent miRNAdisease associations from the view of functional module. To understand the miRNA functional module in diseases, the case study is presented. We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm. Experimental results showed that our method cannot only outperform classical algorithms, such as K-means, IK-means, MCODE, HC-PIN, and ClusterONE, but can also achieve an ideal overall performance in terms of a composite score consisting of f1, Sensitivity, and Accuracy. Altogether, our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.

Energy-efficient project scheduling with supplier selection in manufacturing projects

In make-to-order or engineer-to-order systems, the overarching process of producing complex and highly customized products along with managing multiple stakeholders (including suppliers) can be considered to be a project scheduling problem. Yet, despite the need for an advanced project scheduling plan for manufacturing, there is little research in the literature considering both project scheduling and supplier selection in a manufacturing context. Due to the scarcity of resources in manufacturing, this project scheduling problem resembles the well-known resource constrained project scheduling problem (RCPSP). Additionally, with the increasing awareness of the environment and the need to minimize energy consumption and noise pollution, and continuing concern with worker safety, there is a need for innovative methods to improve green factors (energy, noise, and safety) in manufacturing projects. This paper, therefore, proposes for manufacturing projects an energy-efficient resource constrained project scheduling plan embedded with a supplier selection strategy called the green RCPSP for manufacturing (or GRCPSPM). This proposed GRCPSPM is designed as a bi-objective problem with the conjoint objectives of minimization of project completion time and green project indicators (GPI). To solve that bi-objective problem, a genetic algorithm-based memetic algorithm (MA) is proposed and experimental results show that the proposed MA outperforms the well-known non-dominated sorting genetic algorithm-II (NSGA-II) approach and evolutionary programming (EP) algorithm for a number of self-generated project scheduling instances, in terms of both solution quality and computational efficiency. The obvious outcome of this study is to support the selection of an appropriate supplier based on resource processing speeds and resource GPI, thereby ensuring green manufacturing project scheduling with minimum completion time and minimum energy usage.

Evolutionary Priority-Based Dynamic Programming for the Adaptive Integration of Intermittent Distributed Energy Resources in Low-Inertia Power Systems

The variability and uncertainty caused by the increased penetrations of renewable energy sources must be properly considered in day-ahead unit commitment, optimal power flow, and even real-time economic dispatch problems. Besides achieving minimum cost, modern generation schedules must satisfy a larger set of different complex constraints. These account for the generation constraints in the presence of renewable generation, network constraints affected by the distributed energy resources, bilateral contracts enclosing independent capacity provision, ancillary power auctions, net-metering and feed-in-tariff prosumers, and corrective security actions in sudden load variations or outage circumstances. In this work, a new method is presented to appropriately enhance the integration of distributed energy resources in low-inertia power grids. Based on optimal unit commitment schedules derived from priority-based dynamic programming, the potential of increasing the renewable capacity was examined, performing simulations for different scenarios. To ameliorate the expensive requirement of computational complexity, this approach aimed at eliminating the increased exploration-exploitation efforts. On the contrary, its promising solution relies on the evolutionary commitment of the next optimum configuration based on priority-list schemes to accommodate the intermittent generation progressively. This is achieved via the collection of mappings that transform many-valued clausal forms into satisfiability equivalent Boolean expressions.

Evolutionary algorithms for modeling non-equilibrium population

During protein synthesis the genetic code links each codon, a triplet of nucleotides, with the corresponding amino acid. Synonymous codons are those that code for the same amino acid. The difference in the frequency of occurrence of certain synonymous codons over other synonymous codons is called the codon usage bias (CUB). The Zeng and Charlesworth model is used to estimate the strength of CUB. In their model the evolutionary process is represented by a Markov model, which allows the population size to vary over time. In this paper we propose a new method that incorporates demographic changes into the model. The method is a hybrid of two optimizers, the first is evolutionary programming and the second is a version of the genetic algorithms that uses chromosomes of variable lengths, which allows for expressing more demographic changes than what the simplified model presented by Zeng and Charlesworth does. We conduct several simulations to show why this hybridization is necessary, and also to show the superior performance of this new hybrid.

Effective decoders for DNA codes

A number of applications use DNA as a storage mechanism. Because processes in these applications may cause errors in the data, the information must be encoded as one of a chosen set of words that are well separated from one another — a DNA error-correcting code. Typically, the types of errors that may occur include insertions, deletions and substitutions of symbols, making the edit metric the most suitable choice to measure the distance between strings. Decoding, the process of recovering the original word when errors occur, is complicated by biological restrictions combined with a high cost to calculate edit distance.Side effect machines (SEMs), an extension of finite state machines, can provide efficient decoding algorithms for such codes. Several codes of varying lengths are used to study the effectiveness of evolutionary programming (EP) as a general approach for finding SEMs for edit metric decoding. Two classification methods (direct and fuzzy classification) are compared, and different EP settings are examined to observe how decoding accuracy is affected. Regardless of code length, the best results are found using fuzzy classification. The best accuracy is seen for codes of length 10, for which a maximum accuracy of up to 99.4% is achieved for distance 1 and distance 2 and 3 achieve up to 97.1% and 85.9%, respectively. Additionally, the SEMs are examined for potential bloat by comparing the number of reachable states against the total number of states. Bloat is seen more in larger machines than in smaller machines. Furthermore, the results are analysed to find potential trends and relationships among the parameters, with the most consistent trend being that, when allowed, the longer codes generally show a propensity for larger machines.

Coordinated Design of FACTS Controller with PSS for Stability Enhancement Using a Novel Hybrid Whale Optimization Algorithm – Nelder Mead Approach

This article investigates a novel cascaded fractional order PI-PD structure for power system stabilizers and flexible AC transmission system-based damping controllers to enhance the stability of the power system. The proposed controller parameters are optimized by a hybrid Modified Whale Optimization Algorithm (MWOA) with the Nelder-Mead algorithm. The novel hybrid algorithm accomplishes a proper balance between the exploration and exploitation phases of the modified whale optimization algorithm. This capability of the hybrid technique is certified by using the benchmark test functions compared with that of an MWOA, WOA, gravitational search algorithm, fast evolutionary programming, particle swarm optimization, genetic algorithm, and differential evolution. The proposed controller is optimized and verified under various loading conditions using a hybrid technique. Furthermore, to demonstrate its superiority, the results of the proposed hybrid algorithm are compared with recently published MWOA, WOA, and well-established genetic algorithms.

Fuel constrained short-term hydrothermal generation scheduling

Due to gradually diminution of fossil fuel, the cost-effective utilization of available fuel for power generation has turn out to be a vital concern of electric power utilities. Thermal power plants have to operate within their fuel confines and contractual constraints. This work suggests social group entropy optimization (SGEO) technique to solve short-term generation scheduling of a power system consisting of fuel constrained thermal generating units, cascaded hydro power plants, solar PV plants, wind turbine generators and pumped storage hydro (PSH) plants with demand side management (DSM). Simulation results of the test system have been compared with those acquired by self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE). Numerical results show that fuel consumption can be adequately controlled for fulfilling constraints imposed by suppliers and total cost with fuel constraints is more than the cost without fuel constraints. It has been also observed from the comparison that the suggested SGEO has the ability to bestow with superior-quality solution.

Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming

Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limitations and does not display outstanding performance on some benchmark functions. In this work, we proposed a novel mutation strategy based on both the “step size” and “survival rate” for EP (SSMSEP). SSMSEP-1 and SSMSEP-2 are two variants of SSMSEP, which use “survival rate” or “step size” separately. Our proposed method can select appropriate mutation operators and update parameters for mutation operators according to diverse landscapes during the evolutionary process. Compared with SSMSEP-1, SSMSEP-2, SSEP, and other EP variants, the SSMSEP demonstrates its robustness and stable performance on most benchmark functions tested.

Renewable energy incorporating short‐term optimal operation using oppositional grasshopper optimization

AbstractRenewable energy‐based hydro‐thermal scheduling is a new assignment in solar‐wind‐hydro power structures including thermal plants with non‐convex fuel costs, a time delay of the multi‐reservoir cascaded hydro unit, generating units for wind power, and photo‐voltaic plant of the solar system. Renewable energy resources are used in immense quantity as they are naturally accessible and charge‐free. In this regard, this article presents a single‐objective economic replica of short‐term hydro‐thermal scheduling (HTS) problems having renewable solar and wind units. To speed up the convergence swiftness, of OBL is incorporated with the fundamental grasshopper optimization algorithm (GOA) method which is actively associated with the social communication of the grasshopper in the environment. Furthermore, HTS and hydro thermal scheduling incorporating solar and wind energy are considered for the benchmark test systems. Results presented by a few recent techniques (like fuzzy based evolutionary programming, teaching learning‐based optimization, etc.) have been compared with those obtained by the oppositional GOA (OGOA) to set up its effectiveness. Simulation results of OGOA technique clearly show that the renewable solar and wind units can significantly reduce the fuel cost of the power systems.

The proper role of history in evolutionary explanations

AbstractEvolutionary explanations are not only common in the biological sciences, but also widespread outside biology. But an account of how evolutionary explanations perform their explanatory work is still lacking. This paper develops such an account. I argue that available accounts of explanations in evolutionary science miss important parts of the role of history in evolutionary explanations. I argue that the historical part of evolutionary science should be taken as having genuine explanatory force, and that it provides how‐possibly explanations sensu Dray. I propose an account of evolutionary explanations as comparative‐composite explanations consisting of two distinct kinds of explanations, one processual and one historical, that are connected via the explanandum's evolvability to show how the explanandum is the product of its evolutionary past. The account is both a reconstruction of how evolutionary explanations in biology work and a guideline specifying what kind of explanations evolutionary research programs should develop.

Optimizing network microsegmentation policy for cyber resilience

This paper describes an approach for improving cyber resilience through the synthesis of optimal microsegmentation policy for a network. By leveraging microsegmentation security architecture, we can reason about fine-grained policy rules that enforce access for given combinations of source address, destination address, destination port, and protocol. Our approach determines microsegmentation policy rules that limit adversarial movement within a network according to assumed attack scenarios and mission availability needs. For this problem, we formulate a novel optimization objective function that balances cyberattack risks against accessibility to critical network resources. Given the application of a particular set of policy rules as a candidate optimal solution, this objective function estimates the adversary effort for carrying out a particular attack scenario, which it balances against the extent to which the solution restricts access to mission-critical services. We then apply artificial intelligence techniques (evolutionary programming) to learn microsegmentation policy rules that optimize this objective function.

A Multiscale Optimization Technique for Large-Scale Subsurface Profiling

An efficient multiresolution inverse scattering approach is presented for profiling high-contrast buried targets in large investigation domains (IDs). The proposed technique is based on an iterative multiscale approach (IMSA) that starts with coarse meshes and successively zooms in and marches toward the detected target location. Hence, by tightening the ID, the resolution could be enhanced without any increase in the number of meshes of the search domain. Here, the global evolutionary programming (EP) optimization algorithm is used in each step of IMSA, to guarantee the success of inversion process. In particular, an efficient variation in EP with Cauchy mutation is implemented for enhanced convergence. Moreover, a hybrid combination of multifrequencies (MFs) and frequency hopping (FH) schemes is combined with the proposed technique to better deal with the problem’s nonlinearity and also simultaneously make a consistent trade-off between mesh length and frequencies. The proposed FH-MF-IMSA is evaluated by implementing it to various large-scale subsurface problems. Furthermore, the superior performance of this technique is shown by comparing its results with the standard IMSA, as well as with other well-known global optimization techniques.

Horse Herd Optimization Algorithm for Fuel Constrained Day-ahead Scheduling of Isolated Nanogrid

ABSTRACT Day-ahead scheduling of isolated nanogrid (NG) is an important task in power system. Owing to slowly lessening of fossil fuel, the profitable use of available fuel for electric power generation has turn out to be an extremely important concern of electric power utilities. This paper suggests horse herd optimization algorithm (HOA) to solve fuel constrained day-ahead scheduling of isolated nanogrid (NG) for five neighboring homes. NG comprises diesel generators, solar PV plants, battery energy storage system (BESS) and plug-in electric vehicles (PEVs). Simulation results of the test system have been compared with those obtained from social group entropy optimization, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, fast convergence evolutionary programming and differential evolution. It has been observed from the comparison that the suggested HOA has the capacity to give better solution.

Load shedding scheme based metaheuristic technique for power system controlled islanding

<p>Power system-controlled islanding is one of the mitigation techniques taken to prevent blackouts during severe outage. The implementation of controlled islanding will lead to the formation of few islands, that can operate as a stand-alone island. However, some of these islands may not be balanced in terms of generation and load after the islanding execution. Therefore, a good load shedding scheme is required to meet the power balance criterion so that it can operate as a balanced stand-alone island. Thus, this paper developed a load shedding scheme-based metaheuristics technique namely modified discrete evolutionary programming (MDEP) technique to determine the optimal amount of load to be shed in order to produce balanced stand-alone islands. The developed load shedding scheme is evaluated and validated with two other load shedding techniques which are conventional EP and exhaustive search techniques. The IEEE 30-bus and 39-bus test systems were utilized for this purpose. The results proves that the load shedding based MDEP technique produces the optimal amount of loads to be shed with shortest computational time as compared with the conventional EP and exhaustive search techniques.</p>

Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming

Genetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Lévy distribution. This study proposes a hyper-heuristic approach that employs GP to automatically design different mutation operators for EP. At each generation, the EP algorithm can adaptively explore the search space according to historical information. The experimental results demonstrate that the EP with adaptive mutation operators, designed by the proposed hyper-heuristics, exhibits improved performance over other EP versions (both manually and automatically designed). Many researchers in evolutionary computation advocate adaptive search operators (which do adapt over time) over non-adaptive operators (which do not alter over time). The core motive of this study is that we can automatically design adaptive mutation operators that outperform automatically designed non-adaptive mutation operators.

A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

A Non-Convex Economic Dispatch Problem with Point-Valve Effect Using a Wind-Driven Optimisation Approach.

This study presents the efficiency of the wind-driven optimisation (WDO) approach in solving non-convex economic dispatch problems with point-valve effect. The best economic dispatch for a power system is one wherein the system can generate energy at a low cost. The calculation of the generating cost is subject to a number of constraints, such as the power demand for the entire system and the generation limit for each generator unit in the system. In addition, the system should also produce low power loss. The WDO optimisation technique is developed based on the concept of natural wind movement, which serves as a stabiliser to equalise the inequality of air pressure in the atmosphere. One major advantage of WDO over other techniques is its search accuracy. The proposed algorithm has been implemented in two systems, namely, the 10-generator and 40-generator systems. Both systems were tested in a Matlab environment. To highlight the capabilities of WDO, the results using this proposed technique are compared with the results obtained using flower pollination algorithm, moth flame optimisation, particle swarm optimisation and evolutionary programming techniques to determine the efficiency of the proposed approach in solving economic dispatch. The simulation results show the capability of WDO in determining the optimal power generation value with minimum generation cost and low rate of power loss.

Algorithmic model of social processes

The development of the social sciences needs to rely on precise methods. The nomological model of explanation adopted in the natural sciences is ill-suited for the social sciences. An algorithmic model of society can be a promising solution to existing problems. In its most general form, an algorithm is a generally understood prescription for what actions to perform and in what order to achieve the desired result. Any algorithm can be represented as a set of rules of the form «If A, do D to get P». People are the bearers of this kind of rules that apply in different areas of their activities. The rules are subject to change based on personal and collective experience. There is a special mathematical discipline that studies the laws of evolution of such rules. This discipline is called genetic (evolutionary) programming. Contrary to the threatening name, the algorithmic model does not imply the deprivation of a person’s right to free choice, but it needs this right as a necessary condition for the evolution of social algorithms. These algorithms allow us to give a non-causal, but law-like explanation of many well-known social phenomena, as well as to effectively model the future, which is critically important today. A retrospective look at the evolution of social algorithms shows that the current global crisis of human society is associated with the approach to the point of singularity in their evolution. This is due to the fact that there is no need for direct human participation in the implementation of social algorithms, which is reflected in a fundamental change in the sphere of employment and less need for further development of the sciences.

Polyaneuploid Cancer Cell Dormancy: Lessons From Evolutionary Phyla

Dormancy is a key survival strategy in many organisms across the tree of life. Organisms that utilize some type of dormancy (hibernation, aestivation, brumation, diapause, and quiescence) are able to survive in habitats that would otherwise be uninhabitable. Induction into dormant states is typically caused by environmental stress. While organisms are dormant, their physical activity is minimal, and their metabolic rates are severely depressed (hypometabolism). These metabolic reductions allow for the conservation and distribution of energy while conditions in the environment are poor. When conditions are more favorable, the organisms are then able to come out of dormancy and reengage in their environment. Polyaneuploid cancer cells (PACCs), proposed mediators of cancer metastasis and resistance, access evolutionary programs and employ dormancy as a survival mechanism in response to stress. Quiescence, the type of dormancy observed in PACCs, allows these cells the ability to survive stressful conditions (e.g., hypoxia in the microenvironment, transiting the bloodstream during metastasis, and exposure to chemotherapy) by downregulating and altering metabolic function, but then increasing metabolic activities again once stress has passed. We can gain insights regarding the mechanisms underlying PACC dormancy by looking to the evolution of dormancy in different organisms.