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
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Second international workshop on Intelligent systems design and application
Using predators and preys in evolution strategies
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Local search for multiobjective function optimization: pareto descent method
Proceedings of the 8th annual conference on Genetic and 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
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A NSGA-II, web-enabled, parallel optimization framework for NLP and MINLP
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A new memetic strategy for the numerical treatment of multi-objective optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Adapting to the Habitat: On the Integration of Local Search into the Predator-Prey Model
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
A real-coded predator-prey genetic algorithm for multiobjective optimization
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Designing multi-objective variation operators using a predator-prey approach
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
On gradient based local search methods in unconstrained evolutionary multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Design issues in a multiobjective cellular genetic algorithm
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Accelerating population-based search heuristics by adaptive resource allocation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Over the last decade, the predator---prey model (PPM) has emerged as an alternative algorithmic approach to multi-objective evolutionary optimization, featuring a very simple abstraction from natural species interplay and extensive parallelization potential. While substantial research has been done on the former, we for the first time review the PPM in the light of parallelization: We analyze the architecture and classify its components with respect to a recent taxonomy for parallel multi-objective evolutionary algorithms. Further, we theoretically examine benefits of simultaneous predator collaboration on a spatial population structure and give insights into solution emergence. On the prey level, we integrate a gradient-based local search mechanism to exploit problem independent parallelization and hybridize the model in order to achieve faster convergence and solution stability. This way, we achieve a good approximation and unfold further parallelization potential for the model.