4 research outputs found
Comparing and Combining Lexicase Selection and Novelty Search
Lexicase selection and novelty search, two parent selection methods used in
evolutionary computation, emphasize exploring widely in the search space more
than traditional methods such as tournament selection. However, lexicase
selection is not explicitly driven to select for novelty in the population, and
novelty search suffers from lack of direction toward a goal, especially in
unconstrained, highly-dimensional spaces. We combine the strengths of lexicase
selection and novelty search by creating a novelty score for each test case,
and adding those novelty scores to the normal error values used in lexicase
selection. We use this new novelty-lexicase selection to solve automatic
program synthesis problems, and find it significantly outperforms both novelty
search and lexicase selection. Additionally, we find that novelty search has
very little success in the problem domain of program synthesis. We explore the
effects of each of these methods on population diversity and long-term problem
solving performance, and give evidence to support the hypothesis that
novelty-lexicase selection resists converging to local optima better than
lexicase selection
Lexicase selection in Learning Classifier Systems
The lexicase parent selection method selects parents by considering
performance on individual data points in random order instead of using a
fitness function based on an aggregated data accuracy. While the method has
demonstrated promise in genetic programming and more recently in genetic
algorithms, its applications in other forms of evolutionary machine learning
have not been explored. In this paper, we investigate the use of lexicase
parent selection in Learning Classifier Systems (LCS) and study its effect on
classification problems in a supervised setting. We further introduce a new
variant of lexicase selection, called batch-lexicase selection, which allows
for the tuning of selection pressure. We compare the two lexicase selection
methods with tournament and fitness proportionate selection methods on binary
classification problems. We show that batch-lexicase selection results in the
creation of more generic rules which is favorable for generalization on future
data. We further show that batch-lexicase selection results in better
generalization in situations of partial or missing data.Comment: Genetic and Evolutionary Computation Conference, 201