Name
Abstract | ||
|---|---|---|
ABSTRACTLearning has been shown to be beneficial to an evolutionary process through the Baldwin Effect. Moreover, learning can be classified into two categories: asocial learning, e.g. trial-and-error; and social learning, e.g. imitation learning. A learning strategy, or learning rule - a combination of individual and social learning --- has been suggested by recent research can be more adaptive than both social and individual learning alone. However, this also leaves open an important question as to how best to combine these forms of learning in different environments. This paper investigates this question under a dynamic rugged landscape (i.e. dynamic NK-landscape). Experimental results show that a learning strategy is able to promote an evolving population better, resulting in higher average fitness, over a series of changing environments than asocial learning alone. The results also show that the population of strategic learners maintains a higher proportion of plasticity --- the ability to change the phenotype in response to environmental challenges --- than the population of individual learners alone. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3321707.3321741 | Genetic and Evolutionary Computation Conference |
Keywords | Field | DocType |
Baldwin Effect, Phenotypic Plasticity, Evolutionary Algorithms, Dynamic Environment, NK-Landscape | Computer science,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nam D. Le | 1 | 3 | 5.44 |
| Michael O'Neill | 2 | 876 | 69.58 |
| Anthony Brabazon | 3 | 918 | 98.60 |