Myths and Legends of the Baldwin Effect

TL;DR

This paper clarifies common misconceptions about the Baldwin effect, emphasizing its complexity involving costs and benefits of lifetime learning, and distinguishes it from Lamarckian inheritance and memetic evolution.

Contribution

It provides a nuanced explanation of the Baldwin effect, highlighting overlooked costs and benefits, and clarifies its distinction from Lamarckian inheritance in evolutionary computation.

Findings

The Baldwin effect involves both costs and benefits of lifetime learning.

It is not equivalent to Lamarckian inheritance.

Lamarckian algorithms are not generally superior for evolutionary problems.

Abstract

This position paper argues that the Baldwin effect is widely misunderstood by the evolutionary computation community. The misunderstandings appear to fall into two general categories. Firstly, it is commonly believed that the Baldwin effect is concerned with the synergy that results when there is an evolving population of learning individuals. This is only half of the story. The full story is more complicated and more interesting. The Baldwin effect is concerned with the costs and benefits of lifetime learning by individuals in an evolving population. Several researchers have focussed exclusively on the benefits, but there is much to be gained from attention to the costs. This paper explains the two sides of the story and enumerates ten of the costs and benefits of lifetime learning by individuals in an evolving population. Secondly, there is a cluster of misunderstandings about the relationship between the Baldwin effect and Lamarckian inheritance of acquired characteristics. The Baldwin effect is not Lamarckian. A Lamarckian algorithm is not better for most evolutionary computing problems than a Baldwinian algorithm. Finally, Lamarckian inheritance is not a better model of memetic (cultural) evolution than the Baldwin effect.