A New Approach to Evolutionary Computation: Segregative Genetic Algorithms (SEGA)
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Frequency estimation of undamped exponential signals using genetic algorithms
Computational Statistics & Data Analysis
Engineering Applications of Artificial Intelligence
A parallel hybrid implementation using genetic algorithm, GRASP and reinforcement learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A hybrid genetic algorithm for the capacitated vehicle routing problem
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A case for codons in evolutionary algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Solving nonlinear optimal control problems using a hybrid IPSO-SQP algorithm
Engineering Applications of Artificial Intelligence
Applications of evolutionary SVM to prediction of membrane alpha-helices
Expert Systems with Applications: An International Journal
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From the Publisher:The mathematics employed by genetic algorithms (GAs)are among themost exciting discoveries of the last few decades. But what exactly is a genetic algorithm? A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. It applies the rules of reproduction, gene crossover, and mutation to pseudo-organisms so those "organisms" can pass beneficial and survival-enhancing traits to new generations. GAs are useful in the selection of parameters to optimize a system's performance. A second potential use lies in testing and fitting quantitative models. Unlike any other book available, this interesting new text/reference takes you from the construction of a simple GA to advanced implementations. As you come to understand GAs and their processes, you will begin to understand the power of the genetic-based problem-solving paradigms that lie behind them. The Practical Handbook of Genetic Algorithms presents for the first time new areas of research and implementation. Problems that for many have been considered intractable are shown to be solvable using the techniques described in this work. Specific solution descriptions to real-world problems are provided, or use these as examples to develop solutions to unique problems. Volume II picks up where the first book leaves off and presents the topic from more of an applications point of view. The focus of the book is to show the reader how to develop their own genetic algorithm coding schemes and how and when to employ the GA to solve problems.