No Species Is an Island, Entire of Itself: Genetic Speciation Algorithm with Interplay among Species

International Journal of Engineering, Transactions B: Applications. 2027. Vol. 40. No. 5. P. 1437–1460.

This paper, with a particular emphasis on Genetic Algorithm (GA), proposes a novel approach that integrates clustering mechanisms, predator-prey dynamics, and a distinctive selection strategy for generating subsequent populations. The proposed evolutionary algorithm, referred to as Genetic Speciation Algorithm with Interplay Among Species (GSAIS), is comprehensively evaluated against several variants of GAs as well as a set of widely adopted state-of-the-art optimization algorithms, recognized among the top-performing winners of recent Congress on Evolutionary Computation (CEC) competitions. This thorough comparative study is performed using the most well-established benchmark functions, covering unimodal, multimodal, hybrid, and composition optimization landscapes. The experiments are conducted on medium-, and large-scale problem instances to evaluate the robustness, scalability, convergence behavior, and solution quality of GSAIS across diverse optimization scenarios. The GSAIS achieved rank 2nd, and 1st against the competitors in medium- and large- scale benchmark problems, respectively based on the Friedman Test with receiving 40.3% of top ranking along all instances based on best found and standard deviation rankings. Extensive statistical and computational results demonstrate the effectiveness of GSAIS, highlighting its superior performance in achieving global optimization. The ablation study and sensitivity analysis reveals the positive effect of the incorporated mechanisms in GSAIS in finding global optimization, especially in large scale problems. The findings establish GSAIS as a potential candidate method for addressing optimization problems and underscore its potential to advance the design of next-generation evolutionary algorithms.

Keywords: Metaheuristics, Genetic Algorithm, Genetic Speciation Algorithm, Evolutionary Computation, Global Optimization