Massively-concurrent Agent-based Evolutionary Computing

TL;DR

This paper introduces a new asynchronous agent-based evolutionary algorithm that leverages modern functional programming technologies, resulting in faster and more efficient optimization solutions compared to previous methods.

Contribution

The paper presents a novel fully asynchronous multi-agent evolutionary algorithm implemented with Erlang and Scala, overcoming previous technological limitations.

Findings

Asynchronous agents improve optimization speed.

Functional language implementation enhances efficiency.

Algorithm outperforms traditional methods in experiments.

Abstract

The fusion of the multi-agent paradigm with evolutionary computation yielded promising results in many optimization problems. Evolutionary multi-agent system (EMAS) are more similar to biological evolution than classical evolutionary algorithms. However, technological limitations prevented the use of fully asynchronous agents in previous EMAS implementations. In this paper we present a new algorithm for agent-based evolutionary computations. The individuals are represented as fully autonomous and asynchronous agents. An efficient implementation of this algorithm was possible through the use of modern technologies based on functional languages (namely Erlang and Scala), which natively support lightweight processes and asynchronous communication. Our experiments show that such an asynchronous approach is both faster and more efficient in solving common optimization problems.