Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
Journal of Heuristics
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Forking Genetic Algorithm with Blocking and Shrinking Modes (fGA)
Proceedings of the 5th International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
A Religion-Based Spatial Model for Evolutionary Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Diploid Genetic Algorithm for Preserving Population Diversity - pseudo-Meiosis GA
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Genetic Operators in a Dual Genetic Algorithm
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
PDGA: the primal-dual genetic algorithm
Design and application of hybrid intelligent systems
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
The influence of migration sizes and intervals on island models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Multiobjective genetic algorithm-based fuzzy clustering of categorical attributes
IEEE Transactions on Evolutionary Computation
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
On set-based multiobjective optimization
IEEE Transactions on Evolutionary Computation
Bi-objective multipopulation genetic algorithm for multimodal function optimization
IEEE Transactions on Evolutionary Computation
A favorable weight-based evolutionary algorithm for multiple criteria problems
IEEE Transactions on Evolutionary Computation
A novel set-based particle swarm optimization method for discrete optimization problems
IEEE Transactions on Evolutionary Computation
A dual-population genetic algorithm for adaptive diversity control
IEEE Transactions on Evolutionary Computation
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
On the role of population size and niche radius in fitness sharing
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Where Are the Niches? Dynamic Fitness Sharing
IEEE Transactions on Evolutionary Computation
Evolutionary learning of hierarchical decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary optimization of a hierarchical object recognition model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary optimization of radial basis function classifiers for data mining applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary neural networks for anomaly detection based on the behavior of a program
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving Fuzzy Rules for Relaxed-Criteria Negotiation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Avoiding premature convergence to local optima and rapid convergence towards global optima has been the major concern with evolutionary systems research. In order to avoid premature convergence, sufficient amount of genetic diversity within the evolving population is considered necessary. Several studies have focused to devise techniques to control and preserve population diversity throughout the evolution. Since mutation is the major operator in many evolutionary systems, such as evolutionary programming and evolutionary strategies, a significant amount of research has also been done for the elegant control and adaptation of the mutation step size that is proper for traversing across the locally optimum points and reach for the global optima. This paper introduces Diversity Guided Evolutionary Programming, a novel approach to combine the best of both these research directions. This scheme incorporates diversity guided mutation, an innovative mutation scheme that guides the mutation step size using the population diversity information. It also takes some extra diversity preservative measures to maintain adequate amount of population diversity in order to assist the proposed mutation scheme. An extensive simulation has been done on a wide range of benchmark numeric optimization problems and the results have been compared with a number of recent evolutionary systems. Experimental results show that the performance of the proposed system is often better than most other algorithms in comparison on most of the problems.