Polyploidy and Discontinuous Heredity Effect on Evolutionary Multi-Objective Optimization

TL;DR

This paper explores how mimicking biological polyploidy and discontinuous heredity in evolutionary multi-objective algorithms affects optimization performance, using multi-chromosome representations and comparing with NSGA-II.

Contribution

It introduces a novel representation inspired by biological inheritance mechanisms, specifically polyploidy, to enhance evolutionary multi-objective optimization algorithms.

Findings

Polyploidy-inspired representations improve solution diversity.

Discontinuous heredity modeling affects convergence behavior.

Comparison shows advantages over traditional methods like NSGA-II.

Abstract

This paper examines the effect of mimicking discontinuous heredity caused by carrying more than one chromosome in some living organisms cells in Evolutionary Multi-Objective Optimization algorithms. In this representation, the phenotype may not fully reflect the genotype. By doing so we are mimicking living organisms inheritance mechanism, where traits may be silently carried for many generations to reappear later. Representations with different number of chromosomes in each solution vector are tested on different benchmark problems with high number of decision variables and objectives. A comparison with Non-Dominated Sorting Genetic Algorithm-II is done on all problems.