This book can be accessed from the link below - Copyright @ 2007 Springer-Verla

Jin, Y

Ong, Y

Yang, S

English

Brunel University Research Archive

414 Evolutionary Computation Volume 16, Number 3GAs with Memory- and Elitism-Based Immigrants in Dynamic Environments

A comparative study of steady state and generational genetic algorithms for use in nonstationary environments. In

A comparison of dominance mechanisms and simple mutation on non-stationary problems.

A multi-population approach to dynamic optimization problems.

A new diploid scheme and dominance change mechanism for non-stationary function optimisation.

A new population based adaptive dominance change mechanism for diploid genetic algorithms in dynamic environments.

A test problem generator for non-stationary environments.

Adaptation in Natural and Artiﬁcial Systems.

Adaptation to changing environments by means of the memory based thermodynamical genetic algorithm.

An immune system-based genetic algorithm to deal with dynamic environments: Diversity and memory.

Case-based initialization of genetic algorithms.

Designing Evolutionary Algorithms for Dynamic Environments.

Dynamic memory model for non-stationary optimization.

Evolutionary Algorithms and Dynamic Optimization Problems. Der andere Verlag, Osnabr¨ uck,

Evolutionary Computation Volume 16, Number 3 415S. Yang Y a n g ,S .a n dT i n ´ os,

Evolutionary Optimization in Dynamic Environments.

Evolutionary optimization in non-stationary environments.

Evolvability in dynamic ﬁtness landscapes: A genetic algorithm approach.

Experimental study on population-based incremental learning algorithms for dynamic optimization problems.

Genetic algorithms for open shop scheduling and re-scheduling.

Genetic algorithms for tracking changing environments.

Genetic algorithms with elitism-based immigrants for changing optimization problems.

Improving the genetic algorithm’s performance when using transformation.InProceedingsofthe6thInternationalConferenceonNeuralNetworksandGenetic Algorithms,

Memory enhanced evolutionary algorithms for changing optimization problems.

Memory-based immigrants for genetic algorithms in dynamic environments.

Memory-enhanced univariate marginal distribution algorithms for dynamic optimizationproblems.InProceedingsofthe2005CongressonEvolutionaryComputation,V ol.3,

Non-stationary problem optimization using the primal-dual genetic algorithm.

Nonstationary function optimization using genetic algorithms with dominance and diploidy.

Nonstationary function optimization using the structured geneticalgorithm.InR.M¨

On biologically inspired genetic operators: Using transformation in the standard genetic algorithm.

Population-based incremental learning with associative memory for dynamic environments.

Population-based incremental learning with memory scheme for changing environments.

Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning.

Evolutionary computation in dynamic and uncertain environments

This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineering systems is inevitable. Representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums, are presented. "Evolutionary Computation in Dynamic and Uncertain Environments" is a valuable reference for scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, natural computing and evolutionary computation

Yang, Shengxiang

Ong, Yew-Soon

Jin, Yaochu

De Montfort University Open Research Archive

Evolutionary Computation in Dynamic and Uncertain Environments

This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified ..

Ong, YS

Surrey Research Insight

Evolutionary computation in dynamic and uncertain environments

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Brunel University Research Archive

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Surrey Research Insight