Towards a New Evolutionary Computation: Advances in the Estimation of Distribution Algorithms
✍ Scribed by Jose A. Lozano, Pedro Larrañaga, Iñaki Inza, Endika Bengoetxea
- Book ID
- 127446823
- Publisher
- Springer
- Year
- 2006
- Tongue
- English
- Weight
- 6 MB
- Series
- Studies in Fuzziness and Soft Computing
- Edition
- 1
- Category
- Library
- ISBN
- 3540290060
No coin nor oath required. For personal study only.
✦ Synopsis
Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field.This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.
📜 SIMILAR VOLUMES
Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of
Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of