Adapting Heuristic Mastermind Strategies to Evolutionary Algorithms

TL;DR

This paper adapts heuristic strategies from Mastermind puzzle solving to evolutionary algorithms, enhancing their performance by incorporating local entropy into the fitness function, thus bridging classic search methods with nature-inspired algorithms.

Contribution

It introduces a novel approach to integrate heuristic strategies into evolutionary algorithms using local entropy, improving their effectiveness without high computational costs.

Findings

Local entropy improves evolutionary algorithm performance

Heuristic strategies can be adapted to nature-inspired algorithms

Proposed method outperforms random strategies

Abstract

The art of solving the Mastermind puzzle was initiated by Donald Knuth and is already more than 30 years old; despite that, it still receives much attention in operational research and computer games journals, not to mention the nature-inspired stochastic algorithm literature. In this paper we try to suggest a strategy that will allow nature-inspired algorithms to obtain results as good as those based on exhaustive search strategies; in order to do that, we first review, compare and improve current approaches to solving the puzzle; then we test one of these strategies with an estimation of distribution algorithm. Finally, we try to find a strategy that falls short of being exhaustive, and is then amenable for inclusion in nature inspired algorithms (such as evolutionary or particle swarm algorithms). This paper proves that by the incorporation of local entropy into the fitness function of the evolutionary algorithm it becomes a better player than a random one, and gives a rule of thumb on how to incorporate the best heuristic strategies to evolutionary algorithms without incurring in an excessive computational cost.