Open Source Evolutionary Computation with Chips-n-Salsa

TL;DR

This paper introduces the expanded Chips-n-Salsa Java library, now supporting evolutionary algorithms with diverse models, operators, and benchmark problems, emphasizing modularity, reproducibility, and code quality.

Contribution

It extends the Chips-n-Salsa library to include comprehensive evolutionary computation features with flexible interfaces and robust development practices.

Findings

Supports multiple evolutionary models and operators

Includes adaptive mutation and crossover rates

Facilitates reproducible research with open source and build tools

Abstract

When it was first introduced, the Chips-n-Salsa Java library provided stochastic local search and related algorithms, with a focus on self-adaptation and parallel execution. For the past four years, we expanded its scope to include evolutionary computation. This paper concerns the evolutionary algorithms that Chips-n-Salsa now provides, which includes multiple evolutionary models, common problem representations, a wide range of mutation and crossover operators, and a variety of benchmark problems. Well-defined Java interfaces enable easily integrating custom representations and evolutionary operators, as well as defining optimization problems. Chips-n-Salsa's evolutionary algorithms include implementations with adaptive mutation and crossover rates, as well as both sequential and parallel execution. Source code is maintained on GitHub, and immutable artifacts are regularly published to the Maven Central Repository to enable easily importing into projects for reproducible builds. Effective development processes such as test-driven development, as well as a variety of static analysis tools help ensure code quality.