Optimizing Genetic Algorithms Using the Binomial Distribution

TL;DR

This paper introduces a method to optimize genetic algorithms by leveraging binomial distribution sampling, significantly increasing speed while maintaining solution quality, and demonstrates its effectiveness through implementation in an open-source Java library.

Contribution

The paper presents a novel approach to optimize genetic algorithms by applying binomial distribution sampling to key components, improving runtime efficiency.

Findings

Speed of GA increased by orders of magnitude.

Solution quality remains statistically equivalent.

Open-source implementation validated effectiveness.

Abstract

Evolutionary algorithms rely very heavily on randomized behavior. Execution speed, therefore, depends strongly on how we implement randomness, such as our choice of pseudorandom number generator, or the algorithms used to map pseudorandom values to specific intervals or distributions. In this paper, we observe that the standard bit-flip mutation of a genetic algorithm (GA), uniform crossover, and the GA control loop that determines which pairs of parents to cross are all in essence binomial experiments. We then show how to optimize each of these by utilizing a binomial distribution and sampling algorithms to dramatically speed the runtime of a GA relative to the common implementation. We implement our approach in the open-source Java library Chips-n-Salsa. Our experiments validate that the approach is orders of magnitude faster than the common GA implementation, yet produces solutions that are statistically equivalent in solution quality.