A multiset model of multi-species evolution to solve big deceptive problems

TL;DR

This paper introduces SMuGA, a symbiogenetic algorithm combining multiset genetic algorithms with variable-length parasites, proxy evaluation, and phased evolution, achieving significantly improved performance on large deceptive optimization problems.

Contribution

The paper presents SMuGA, a novel symbiogenetic algorithm with variable parasite length, proxy evaluation, and phased evolution, outperforming existing symbiotic algorithms on large-scale deceptive problems.

Findings

SMuGA outperforms state-of-the-art symbiotic algorithms by an order of magnitude.

It effectively optimizes large deceptive functions with linear scaling in iterations.

The phased evolution approach enhances efficiency and solution quality.

Abstract

This chapter presents SMuGA, an integration of symbiogenesis with the Multiset Genetic Algorithm (MuGA). The symbiogenetic approach used here is based on the host-parasite model with the novelty of varying the length of parasites along the evolutionary process. Additionally, it models collaborations between multiple parasites and a single host. To improve efficiency, we introduced proxy evaluation of parasites, which saves fitness function calls and exponentially reduces the symbiotic collaborations produced. Another novel feature consists of breaking the evolutionary cycle into two phases: a symbiotic phase and a phase of independent evolution of both hosts and parasites. SMuGA was tested in optimization of a variety of deceptive functions, with results one order of magnitude better than state of the art symbiotic algorithms. This allowed to optimize deceptive problems with large sizes, and showed a linear scaling in the number of iterations to attain the optimum.