Enhanced Optimization with Composite Objectives and Novelty Selection

TL;DR

This paper introduces a method that combines composite objectives with a novelty-based selection mechanism to improve multi-objective search in deceptive problems, leading to faster and more consistent solutions.

Contribution

It proposes replacing original objectives with their linear combinations and adding a novelty-based selection to enhance search effectiveness in deceptive problems.

Findings

Outperforms standard methods in discovering minimal sorting networks

Finds solutions faster and more consistently

Effective in highly deceptive optimization problems

Abstract

An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful, however: candidates that optimize only one objective while ignoring others are rarely helpful. This paper proposes a solution: The original objectives are replaced by their linear combinations, thus focusing the search on the most useful tradeoffs between objectives. To compensate for the loss of diversity, this transformation is accompanied by a selection mechanism that favors novelty. In the highly deceptive problem of discovering minimal sorting networks, this approach finds better solutions, and finds them faster and more consistently than standard methods. It is therefore a promising approach to solving deceptive problems through multi-objective optimization.