Machine Learning Algorithm for the Fitness Landscape Learning Evolutionary Computation

Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics

Self-adaptive mechanisms for the identification of the most suitable variation operator in Evolutionary meta-heuristics rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel Adaptive Operator Selection mechanism which use...

Learning Representations for Evolutionary Computation

Evolutionary systems have been used in a variety of applications, from turbine design to scheduling problems. The basic algorithms are similar in all these applications, but the representation is always problem specific. Unfortunately, the search time for evolutionary systems very much depends on efficient codings, using problem specific domain knowledge to reduce the size of the search space. ...

Evolutionary Computation for Reinforcement Learning

Algorithms for evolutionary computation, which simulate the process of natural selection to solve optimization problems, are an effective tool for discovering high-performing reinforcement-learning policies. Because they can automatically find good representations, handle continuous action spaces, and cope with partial observability, evolutionary reinforcement-learning approaches have a strong ...

Combining Machine Learning with Evolutionary Computation: Recent Results on LEM

The Learnable Evolution Model (LEM), first presented at the Fourth International Workshop on Multistrategy Learning, employs machine learing to guide evolutionary computation . Specifically, LEM integrates two modes of operation: Machine Learning mode, which employs a machine learning algorithm, and Darwinian Evolution mode, which employs a conventional evolutionary algorithm. The central new i...

Evolutionary Computation in Constraint Satisfaction and Machine Learning