In this paper, we study the relationship between learning and evolution in a simple abstract
model, where neural networks capable of learning are evolved using genetic algorithms …

In this paper, we study the relationship between learning and evolution in a simple abstract
model, where neural networks capable of learning are evolved through genetic algorithms …

The processes of adaptation in natural organisms consist of two complementary phases:
learning, occurring within each individual's lifetime, and evolution, occurring over successive …

In this paper, we describe adaptive processes of populations with two distinct mechanisms
of evolution, Darwinian and Lamarckian. We use a simple abstract model where neural …

The processes of adaptation in a multi-agent system consist of two complementary phases:
1) learning, occurring within each agent's individual lifetime, and 2) evolution, occurring over …

In recent years, studies in epigenetic inheritance in biological systems as well as studies on
evolution in non-biological systems eg, machine learning and robotics, have reopened the …

Learning can increase the rate of evolution of a population of biological organisms (the
Baldwin effect). Our simulations show that in a population of artificial neural networks solving …

This article describes simulations on populations of neural networks that both evolve at the
population level and learn at the individual level. Unlike other simulations, the evolutionary …

This study proposes an adaptive agent as a hybrid of genetic algorithm and neural network,
and to clarify the effectiveness of the combination of two mechanisms in the dynamic …

Using a simple simulation model of evolution and learning, this paper provides an
evolutionary argument why Lamarckian inheritance-the direct transfer of lifetime learning …