Minimal Criterion Coevolution: A New Approach To Open-Ended Search

Keywords

Artificial life; Coevolution; NEAT; Non-objective search; Novelty search; Open-ended evolution

Abstract

Recent studies have emphasized the merits of search processes that lack overarching objectives, instead promoting divergence by rewarding behavioral novelty. While this less objective search paradigm is more open-ended and divergent, it still differs significantly from nature's mechanism of divergence. Rather than measuring novelty explicitly, nature is guided by a single, fundamental constraint: survive long enough to reproduce. Surprisingly, this simple constraint produces both complexity and diversity in a continual process unparalleled by any algorithm to date. Inspired by the relative simplicity of open-endedness in nature in comparison to recent non-objective algorithms, this paper investigates the extent to which interactions between two coevolving populations, both subject to their own constraint, or minimal criterion, can produce results that are both functional and diverse even without any behavior characterization or novelty archive. To test this new approach, a novel maze navigation domain is introduced wherein evolved agents must learn to navigate mazes whose structures are simultaneously coevolving and increasing in complexity. The result is a broad range of maze topologies and successful agent trajectories in a single run, thereby suggesting the viability of minimal criterion coevolution as a new approach to non-objective search and a step towards genuinely open-ended algorithms.

Publication Date

7-1-2017

Publication Title

GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference

Number of Pages

67-74

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3071178.3071186

Socpus ID

85026386409 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85026386409

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