Name
Abstract | ||
|---|---|---|
This paper explores a novel technique for learning the fitness function for search algorithms such as evolutionary strategies and hillclimbing. The aim of the new technique is to learn a fitness function (called a Learned Guidance Function) from a set of sample solutions to the problem. These functions are learned using a supervised learning approach based on deep neural network learning, that is, neural networks with a number of hidden layers. This is applied to a test problem: unscrambling the Rubik's Cube using evolutionary and hillclimbing algorithms. Comparisons are made with a previous LGF approach based on random forests, with a baseline approach based on traditional error-based fitness, and with other approaches in the literature. This demonstrates how a fitness function can be learned from existing solutions, rather than being provided by the user, increasing the autonomy of AI search processes. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1111/exsy.12665 | EXPERT SYSTEMS |
Keywords | DocType | Volume |
artificial intelligence, evolutionary computation, fitness functions, human-like AI, loss functions | Journal | 38 |
Issue | ISSN | Citations |
3 | 0266-4720 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Colin G. Johnson | 1 | 933 | 115.57 |