Evolutionary and Lifetime Learning in Varying NK Fitness Landscape Changing Environments: An Analysis of both Fitness and Diversity

Dara Curran, Colm O'Riordan, Humphrey Sorensen

This paper examines the effects of lifetime learning on populations evolving genetically in a series of changing environments. The analysis of both fitness and diversity of the populations provides an insight into the improved performance provided by lifetime learning. The NK fitness landscape model is employed as the problem task, which has the advantage of being able to generate a variety of fitness landscapes of varying difficulty. Experiments observe the response of populations in an environment where problem difficulty increases and decreases with varying frequency. Results show that lifetime learning is capable of overall higher fitness levels and, in addition, that lifetime learning stimulates the diversity of the population. This increased diversity allows lifetime learning a greater level of recovery and stability than evolutionary learning alone.

Subjects: 7.1 Multi-Agent Systems; 1.9 Genetic Algorithms

Submitted: Apr 24, 2007