The Baldwin Effect: Introduction

The Baldwin effect concerns the trade-offs (the costs and benefits) of learning, in the context of evolution. The Baldwin effect should be of interest to:
  • those who are doing research on Genetic Algorithm / Neural Net (GANN) hybrids or other Genetic Algorithm / Learning Algorithm hybrids
  • those with an interest in Evolutionary Psychology and the relations among learning, instinct, and evolution
  • researchers in machine learning, genetic algorithms, neural networks, evolutionary theory, and cognitive science
  • In 1896, James Mark Baldwin proposed that individual learning can explain evolutionary phenomena that appear to require Lamarckian inheritance of acquired characteristics. The ability of individuals to learn can guide the evolutionary process. In effect, learning smoothes the fitness landscape, thus facilitating evolution. Baldwin further proposed that abilities that initially require learning are eventually replaced by the evolution of genetically determined systems that do not require learning. Thus learned behaviours may become instinctive behaviours in subsequent generations, without appealing to Lamarckian inheritance.