Fine-grained parallel genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
Evolutionary Computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
The effects of varying population density in a fine-grained parallel genetic algorithm
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Inside a predator-prey model for multi-objective optimization: a second study
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
Applied Soft Computing
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Real-World Applications of Multiobjective Optimization
Multiobjective Optimization
PEPPA: a project for evolutionary predator prey algorithms
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Designing multi-objective variation operators using a predator-prey approach
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Engineering Applications of Artificial Intelligence
Group technology based adaptive cell formation using predator-prey genetic algorithm
Applied Soft Computing
Multi-objective optimization using co-evolutionary multi-agent system with host-parasite mechanism
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Parallel predator---prey interaction for evolutionary multi-objective optimization
Natural Computing: an international journal
Hi-index | 0.00 |
This paper proposes a real-coded predator-prey GA for multiobjective optimization (RCPPGA). The model takes its inspiration from the spatial predator-prey dynamics observed in nature. RCPPGA differs itself from previous similar work by placing a specific emphasis on introducing a dynamic spatial structure to the predator-prey population. RCPPGA allows dynamic changes of the prey population size depending on available space and employs a BLX-α crossover operator that encourages a more self-adaptive search. Experiments using two different fitness assignment methods have been carried out, and the results are compared with previous related work. Although RCPPGA does not employ elitism explicitly (such as using an external archive), it has been demonstrated that given a sufficiently large lattice size, RCPPGA can consistently produce and maintain a diverse distribution of nondominated optimal solutions along the Pareto-optimal front even after many generations.