Abstract

Alpha Male Genetic Algorithms are sexist and population based optimization tools that mimic the swarm behavior of animals. The algorithm consists on a socially partitioned population of individuals where the partitions are formed by sexual selection of females. In this paper, we suggest to use Linear Crossover and Hooke-Jeeves method for crossover and hybridization operators of Alpha Male Genetic Algorithms, respectively. We perform a simulation study using a set of well-known test functions to reveal performance differences between the specialized algorithm and some other well-known optimization techniques including Genetic Algorithms, Differential Evolution, Particle Swarm Optimization, and Artificial Bee Colony Optimization. Simulation results show that the specialized algorithm outperforms its counterparts in most of the cases.