Publication

How not to be a black-box: evolution and genetic-engineering of high-level behaviours

Daniel Thalmann
1999
Conference Papers

Abstract

In spite of many success stories in various domains, genetic algorithms and genetic programming still suffer from some significant pitfalls. Those evolved programs often lack important properties such as robustness, comprehensibility, transparency, modifiability and usability of domain knowledge easily available. We attempt to resolve these problems, at least in evolving high-level behaviours, by adopting a technique of conditions-and-behaviours originally used for minimizing the learning space in reinforcement learning. We experimentally validate the approach on a foraging task

Official source

https://infoscience.epfl.ch/entities/publication/e37e83b0-6a92-4dd3-925e-ef3222477fe6

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Daniel Thalmann
1999
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/e37e83b0-6a92-4dd3-925e-ef3222477fe6

About this result

Related concepts (29)

Genetic programming

In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs. The operations are: selection of the fittest programs for reproduction (crossover), replication and/or mutation according to a predefined fitness measure, usually proficiency at the desired task.

Genetic algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

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How not to be a black-box: evolution and genetic-engineering of high-level behaviours

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