Proceedings of the third international conference on Genetic algorithms
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Network random keys: a tree representation scheme for genetic and evolutionary algorithms
Evolutionary Computation
Through the Labyrinth Evolution Finds a Way: A Silicon Ridge
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Redundant Coding of an NP-Complete Problem Allows Effective Genetic Algorithm Search
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A Micro-Genetic Algorithm for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multiobjective Genetic Algorithms for Pump Scheduling in Water Supply
Selected Papers from AISB Workshop on Evolutionary Computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Multi-objective pump scheduling optimisation using evolutionary strategies
Advances in Engineering Software - Special issue on evolutionary optimization of engineering problems
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Genetic drift in genetic algorithm selection schemes
IEEE Transactions on Evolutionary Computation
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
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The application of neutrality is a straightforward tool to preserve population diversity since it allows the genotype (on the represented search space) to be changed without affecting the corresponding fitness. To implement neutrality the literature suggests representational redundancy (more to one correspondence in genotype-phenotype mapping) although using it as a source of neutrality researchers uniformly reported better or worse results. Instead of applying representational redundancy here the utilization of pseudo redundancy as the source of neutrality is proposed, that is, neutrality is achieved by simple objective-fitness transformation while pseudo redundancy (as another redundancy interpretation) denotes more to one correspondence between objective-fitness domains by objective-fitness mapping. The contribution of this work is specified by the dynamic generational gap model introduced for evolutionary algorithms which appears when elitist strategy is used under neutrality by pseudo redundancy. This paper investigates the influence of dynamic generational gap model on the performance of a micro-genetic algorithm framework applied to achieve least cost water pump control policy for an industrial size water network distribution system. The presented constrained mixed-integer optimization problem is originated from the regional water network of the city of Sopron (60,000 citizens) located in Hungary. Here, the goal is to obtain intra-day pump schedule which minimizes the cost required for operation while satisfies the system constraints (water reservoir level limitations, pump flow and delivery regulations, pump energy consumption limitations) and fulfills the water requirement by the users.