Age-layered Population Structure Research Articles

Population Dynamics in Genetic Programming for Dynamic Symbolic Regression

This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of a genetic algorithm (GA) as a traditional evolutionary algorithm and an age-layered population structure (ALPS) as an open-ended evolutionary algorithm for dynamic symbolic regression. This study analyzes population dynamics by examining variable frequencies and impact changes over time in response to dynamic shifts in the training data. The results demonstrate the effectiveness of both the GA and ALPS in handling changing data, showcasing their ability to recover and evolve improved solutions after an initial drop in population quality following data changes. Population dynamics reveal that variable impacts respond rapidly to data changes, while variable frequencies shift gradually across generations, aligning with the indirect measure of fitness represented by variable impacts. Notably, the GA shows a strong dependence on mutation to avoid variables becoming permanently extinct, contrasting with the ALPS’s unexpected insensitivity to mutation rates owing to its reseeding mechanism for effective variable reintroduction.

Optimisation of Multiple Response Processes Using Different Modeling Techniques

Purpose: This article aims to compare the impact on process optimisation with multiple responses of two different mathematical modelling methods: Ordinary Least Squares Method (OLS) and Symbolic Regression Method (SR). Methodology/Approach: Data from the literature were selected from the design of experiments for a process with multiple responses. Using these data, models were obtained that represented each response as a function of independent variables using the OLS and SR techniques. Then, the Desirability method was applied together with the Generalized Reduced Gradient (GRG) in order to obtain the process adjustment that would lead to the optimisation of the responses. Findings: The findings illustrate that the SR modelling technique yields models with superior predictive capabilities when contrasted with the OLS technique. Throughout the optimisation process, it becomes evident that the adjustments in the process diverge, even though the desirability function's value exhibits negligible variation. Research Limitation/implication: This research considered only an SR algorithm and a process with two dependent variables and two independent variables. Originality/Value of paper: No works were found in the literature that reported the use of the Age-Layered Population Structure (ALPS) algorithm in modelling processes that contain multiple responses. Furthermore, no comparison of this method with the OLS method was available. Category: Research paper.

Preset model of bending force in 6-high universal crown tandem cold rolling mill based on symbolic regression

ABSTRACT Highly accurate bending force presetting is a prerequisite to ensure the well flatness of the strip head, however, there are limitations in the preset model used in industrial sites. According to the flatness control characteristics of work roll and intermediate roll bending forces, this paper integrates the historical production data of a 1420 mm 6-high tandem cold rolling mill and establishes a chain model of bending force preset based on symbolic regression, which implements with age-layered population structure genetic programming (ALPS-GPSR). The advantage of symbolic regression models is the ability to obtain explicit mathematical expressions, which is very important in complex industrial production environments. With the application of ALPS-GPSR based bending force preset model in the industrial site, the hit rate of flatness within 6IU in the strip head for three specifications of the strip has increased by 2.86%, 1.61%, and 2.19% respectively on average, achieving a satisfactory result.

Population diversity and inheritance in genetic programming for symbolic regression

AbstractIn this work we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient similarity metrics. Inheritance information is obtained via a full genealogical record of evolution as a directed acyclic graph and a set of methods for extracting relevant patterns. Advances in processing power enable our approach to handle previously infeasible graph sizes of millions of arcs and vertices. To enable a more comprehensive analysis we employ three closely-related but different evolutionary models: canonical GP, offspring selection and age-layered population structure. Our analysis reveals that a relatively small number of ancestors are responsible for producing the majority of descendants in later generations, leading to diversity loss. We show empirically across a selection of five benchmark problems that each configuration is characterized by different rates of diversity loss and different inheritance patterns, in support of the idea that each new problem may require a unique approach to solve optimally.

Implementing Early Warning Systems in WWTP. An investigation with cost-effective LED-VIS spectroscopy-based genetic algorithms

Measuring how the pollution load evolves in real time along sewer networks is key for proper management of water resources and protecting the environment. The technique of molecular spectroscopy for water characterization has increasingly widespread use, as it is a non-invasive technique that leads to the correlation of the physical-chemical conditions of wastewater with spectroscopic surrogates by a series of mathematical estimation models. In the present research work, different symbolic regression models obtained with evolutive genetic algorithms are evaluated for the estimation of chemical oxygen demand (COD); five-day biochemical oxygen demand (BOD5); total suspended solids (TSS); total phosphorus (TP); and total nitrogen (TN), from the spectral response of samples measured between 380 and 700 nm and without the use of chemicals or pre-treatment. Around 650 wastewater samples were used in the campaign, from 43 different wastewater treatment plants (WWTP) in which both, raw/influent and treated/effluent, were examined through 18 models composed of Classical Genetic Algorithm (CGA), the Age-Layered Population Structure (ALPS), and Offspring Selection (OS) by mean of HeuristicLab software, to make a comparison among them and to determine which models and wavelengths are most suitable for the correlation. Models are proposed considering both raw and treated samples together (15) and only with tertiary treated wastewater reclaimed for agriculture irrigation effluent (3). The Pearson correlation coefficients were in the range of 67–91% for the test data in the case of the combined models. The results conform the first steps for a real-time monitoring of WWTP.

Roundabout entry capacity models: genetic programming approach

The aim of this study was to develop three roundabout entry capacity models (RECMs) by employing evolutionary-based regression techniques such as genetic programming (GP), age-layered population structure genetic programming (ALPSGP) and grammatical evolution genetic programming (GEGP) in mixed traffic conditions. Necessary data were collected from 27 roundabouts located in eight states in India. The influence area for gap acceptance method was used to determine the critical gap. To assess the significance of the models and select the best, the modified rank index was applied. The results showed that the GEGP model performed better than the GP and ALPSGP models. The GEGP model is also applicable in practice because of its simplicity. Sensitivity analysis revealed that the critical gap is the prime variable in the development of RECMs. The findings of this study should be useful for traffic planners and designers in the capacity estimation of roundabouts in mixed traffic conditions in developing countries with traffic characteristics similar to those in India.

Enhanced-Hybrid-Age Layered Population Structure (E-Hybrid-ALPS): A Genetic Algorithm with Adaptive Crossover for Molecular Docking Studies of Drug Discovery Process

Objectives: Age Layered Population Structure (ALPS) which introduces time labels into a traditional Genetic Algorithm (GA) is a novel search metaheuristic in overcoming premature convergence There are two models of ALPS namely generational and steady-state with their own merits and demerits Present work has been taken up to devise a search algorithm E-Hybrid-ALPS with the combined concepts and advantages of both the models Methodology: E-Hybrid-ALPS not only combined the concepts and advantages of both the models but also considered weak individual solutions to the mating pool and adaptively applied the crossover operator A search algorithm, a component of the molecular docking tool plays a vital role in the success of molecular docking used in drug discovery Hence, E-Hybrid-ALPS has been implemented as a search algorithm for molecular docking The execution was carried out with two receptor-ligand combinations namely receptor CYP2C8 and ligand Chloroquine, a therapeutic option in the treatment of Corona Virus Disease (COVID-19) and also a drug used in the treatment of Malaria and receptor CYP2B6 and ligand Cyclophosphamide a drug used in the treatment of cancer Results: E-Hybrid-ALPS generates poses of the ligand in the active site of the receptor, calculates the binding energy of each pose and outputs the pose with the lowest binding energy The performance was evaluated by comparing it with the widely used molecular docking tools AutoDock and AutoDockVina which employ Lamarckian GA as a search algorithm Lowest binding energy found by E-Hybrid-ALPS was significantly low as compared to the lowest binding energy found by AutoDock and AutoDockVina Conclusion: E-Hybrid-ALPS which generates a ligand/drug pose with the lowest binding energy can be implemented as a search algorithm for AutoDock molecular docking tool This helps the drug discoverer in designing a drug with a better binding affinity as lower binding energies indicate higher binding affinity © IJCRR

Entry capacity modelling of signalized roundabouts under heterogeneous traffic conditions

ABSTRACTThe primary objectives of this study are to develop two signalized-based roundabouts entry capacity model by employing regression-based multiple non-linear regression model (MNLR) and artificial intelligence-based age-layered population structure genetic programming (ALPS GP) model under heterogeneous traffic conditions. Based on the modified rank index (MRI) values, the ALPS GP model is found to be most suitable model under heterogeneous traffic conditions. However, in a practical point of view, MNLR-based signalized model is recommended for determining roundabout entry capacity under heterogeneous traffic conditions. Sensitivity analysis reports that weaving length is the prime variable and sharing about 27.72 % in the MNLR-based signalized roundabout entry capacity model. These findings will be useful for traffic planners and designers in the capacity estimation of signalized roundabouts under heterogeneous traffic conditions in developing countries with similar traffic characteristics as India.

Trustworthy Genetic Programming-Based Synthesis of Analog Circuit Topologies Using Hierarchical Domain-Specific Building Blocks

This paper presents MOJITO, a system that performs structural synthesis of analog circuits, returning designs that are trustworthy by construction. The search space is defined by a set of expert-specified, trusted, hierarchically-organized analog building blocks, which are organized as a parameterized context-free grammar. The search algorithm is a multiobjective evolutionary algorithm that uses an age-layered population structure to balance exploration versus exploitation. It is validated with experiments to search across >;100 000 different one-stage and two-stage opamp topologies, returning human-competitive results. The runtime is orders of magnitude faster than open-ended systems, and unlike the other evolutionary algorithm approaches, the resulting circuits are trustworthy by construction. The approach generalizes to other problem domains which have accumulated structural domain knowledge, such as robotic structures, car assemblies, and modeling biological systems.