Updating Strategy in Compact Genetic Algorithm Using Moving Average Approach

Abstract

Abstract-The Compact Genetic Algorithm (cGA) has a distinct characteristic that it requires almost minimal memory to store candidate solutions. It represents a population structure as a probability distribution over the set of solutions. Although cGA offers many advantages, it has a limitation that hinges on an assumption of the independency between each individual bit. For example, cGA fails to solve a deceptive function or the so called trap function, which is a standard difficult test problem for genetic algorithm. This paper proposes applying a moving average technique to update a probability vector in the compact genetic algorithm. This method requires fewer evaluations and achieves a higher solution quality. The results are compared with the original cGA, sGA, persistent elitist cGA (pe-cGA) and nonpersistent elitist cGA (ne-cGA). The compared results illustrate that the proposed methodology can successfully improve the solution quality by modifying the updating strategy of cGA.

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

“…All elements of PV are initialised with a value of 0.5 to have an initial uniformly distributed solution. Relevant examples of algorithms using this model are, e.g., [37,[200][201][202][203][204].…”

“…Other studies try to improve the updated process of PV. A moving average strategy is presented in [202], and weights are used in [216]. Learning mechanisms for choosing among multiple evolved probability vectors are also available [217,218].…”

A compact compound sinusoidal differential evolution algorithm for solving optimisation problems in memory-constrained environments

“…All positions of PV are initialised to 0.5 to simulate the usual uniform random initialisation of the basic GA. This model has been used in several compact algorithm proposals based on the binary representation of solutions, including: (Zhou et al, 2002), (Ahn & Ramakrishna, 2003), (Gallagher et al, 2004), (Rimcharoen et al, 2006), (Silva et al, 2007) and (Phiromlap & Rimcharoen, 2013).…”

A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization

“…The cGA manipulates the gene distribution and essentially evolves each gene individually [8]. To enhance cGA's performance, Ahn and Ramakrishna proposed a strong elitism version of cGA [9] and then Rimeharoen et al introduced a moving average technique to update the probability vector [10]. Although the cGA reduces memory requirement and offers many advantages, its limitation is the assumption of the independency between individual genes.…”