Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems

TL;DR

SAFE is a coevolutionary algorithm that simultaneously evolves solutions and objective functions, effectively addressing multiobjective problems without relying on Pareto fronts, demonstrating adaptability and promising results.

Contribution

This paper extends SAFE to multiobjective problems by coevolving candidate solutions and objective functions, offering a novel approach to multiobjective optimization.

Findings

SAFE can identify a set of optimal solutions without Pareto fronts

The coevolution of solutions and objectives is effective for multiobjective problems

Preliminary results show SAFE's adaptability to complex multiobjective tasks

Abstract

We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions. We showed that SAFE was successful at evolving solutions within a robotic maze domain. Herein we present an investigation of SAFE's adaptation and application to multiobjective problems, wherein candidate objective functions explore different weightings of each objective. Though preliminary, the results suggest that SAFE, and the concept of coevolving solutions and objective functions, can identify a similar set of optimal multiobjective solutions without explicitly employing a Pareto front for fitness calculation and parent selection. These findings support our hypothesis that the SAFE algorithm concept can not only solve complex problems, but can adapt to the challenge of problems with multiple objectives.