Genetic Algorithm with Innovative Chromosome Patterns in the Breeding Process

TL;DR

This paper introduces GAB, a novel genetic algorithm variant that enhances exploration and diversity through innovative chromosome patterns, significantly improving performance on complex optimization problems.

Contribution

The paper presents GAB, a new genetic algorithm modification that incorporates border trades to mitigate premature convergence and boost search diversity.

Findings

GAB achieves up to 8x higher fitness scores.

GAB converges 10x faster than standard genetic algorithms.

GAB reliably finds optimal solutions on large-scale problems.

Abstract

This paper proposes Genetic Algorithm with Border Trades (GAB), a novel modification of the standard genetic algorithm that enhances exploration by incorporating new chromosome patterns in the breeding process. This approach significantly mitigates premature convergence and improves search diversity. Empirically, GAB achieves up to 8x higher fitness and 10x faster convergence on complex job scheduling problems compared to standard Genetic Algorithms, reaching average fitness scores of 888 versus 106 in under 20 seconds. On the classic Flip-Flop problem, GAB consistently finds optimal or near-optimal solutions in fewer generations, even as input sizes scale to thousands of bits. These results highlight GAB as a highly effective and computationally efficient alternative for solving large-scale combinatorial optimization problems.