Exploring NK Fitness Landscapes Using Imitative Learning

TL;DR

This paper investigates how imitative learning affects group performance in finding global maxima on NK fitness landscapes, revealing a trade-off between cooperation and diversity that impacts efficiency.

Contribution

It introduces an analysis of cooperative search strategies using imitative learning on NK landscapes, highlighting optimal conditions for improved problem-solving.

Findings

Optimal imitation frequency enhances group performance.

Too much imitation or large groups hinder efficiency on rugged landscapes.

Cooperative groups outperform independent agents under optimal parameters.

Abstract

The idea that a group of cooperating agents can solve problems more efficiently than when those agents work independently is hardly controversial, despite our obliviousness of the conditions that make cooperation a successful problem solving strategy. Here we investigate the performance of a group of agents in locating the global maxima of NK fitness landscapes with varying degrees of ruggedness. Cooperation is taken into account through imitative learning and the broadcasting of messages informing on the fitness of each agent. We find a trade-off between the group size and the frequency of imitation: for rugged landscapes, too much imitation or too large a group yield a performance poorer than that of independent agents. By decreasing the diversity of the group, imitative learning may lead to duplication of work and hence to a decrease of its effective size. However, when the parameters are set to optimal values the cooperative group substantially outperforms the independent agents.