A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments

Abstract

In this paper, an adaptive domination change mechanism for diploid genetic algorithms with discrete representations is presented. It is aimed at improving the performance of existing diploid genetic algorithms in changing environments. Diploidy acts as a source of diversity in the gene pool while the adaptive domination mechanism guides the phenotype towards an optimum. The combined effect of diploidy and the adaptive domination forms a balance between exploration and exploitation. The dominance characteristic of each locus in the population is adapted through feedback from the ongoing search process. A dynamic bit matching benchmark is used to perform controlled experiments. Controlled changes to implement different levels of change severities and frequencies are used. The testing phase consists of four stages. In the first stage, the benefits of the adaptive domination mechanism are shown by testing it against previously proposed diploid approaches. In the second stage, the same adaptive approach is applied to a haploid genetic algorithm to show the effect of the diploidy on the performance of the proposed approach. In the third stage, the levels of diversity introduced by diploidy on the genotype and maintained by the adaptive domination mechanism on the phenotype are explored. In the fourth stage, tests are performed to examine the robustness of the chosen approaches against different mutation rates. Currently, the dominance change mechanism can be applied to diallelic or multiallelic discrete representations and promising results are obtained as a result of the tests performed.

Evolvability and Speed of Evolutionary Algorithms in Light of Recent Developments in Biology

“…Such a mechanism for repression of expression has barely been used in computation. Similar multilayer adaptive encoding schemes have been proposed, for example, the messy Genetic Algorithm (mGA) [164] that combines short building blocks to form variable-length chromosomes to increasingly cover all features of a problem, or diploid Genetic Algorithm, for example, [221] using a two-chromosome representation to adapt phenotypic variation in dynamic environments. However, existing work has not embedded the organizational epigenetic control in algorithms that would allow significant flexibility in changing environments.…”

Memory Based on Abstraction for Dynamic Fitness Functions

“…Implicit memory schemes use redundant encodings, e.g., diploid genotype [10,12,18], to store information for EAs to exploit during the run. In contrast, explicit memory uses precise representations but splits an extra storage space to explicitly store information from a current generation and reuse it later [4,11,17].…”

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

“…In order to address DOPs, many approaches have been developed [41] and can be grouped into four categories: 1) increasing population diversity after a change is detected, such as the adaptive mutation methods [4,33]; 2) maintaining population diversity throughout the run, such as the immigrants approaches [11,39,40]; 3) memory approaches, including implicit [10,32] and explicit memory [2,35,38,43] methods; 4) multi-population [3,24] and speciation approaches [27]. A comprehensive survey on EAs applied to dynamic environments can be found in [14].…”