Bøger i Adaptation, Learning, and Optimization serien

This edited volume is aimed to provide the readers with a brief background of agent based evolutionary search, recent developments and studies dealing with various levels of information abstraction and applications of agent based evo- tionary systems.

This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms.

A combination of theoretical treatment and real-world insight introduce the field of computational intelligence in this valuable reference. Topics include neural networks, frameworks for optimization, parallelization of algorithms, and more.

This edited volume is aimed to provide the readers with a brief background of agent based evolutionary search, recent developments and studies dealing with various levels of information abstraction and applications of agent based evo- tionary systems.

This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

This book focuses on the different steps involved in the conception, implementation and application of Estimation of distribution algorithms (EDAs) that use Markov networks and undirected models in general.

From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more.

In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms.

A combination of theoretical treatment and real-world insight introduce the field of computational intelligence in this valuable reference. Topics include neural networks, frameworks for optimization, parallelization of algorithms, and more.