On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
Phase transition in a random NK landscape model
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Journal of Global Optimization
A study of NK landscapes' basins and local optima networks
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Analysis of estimation of distribution algorithms and genetic algorithms on NK landscapes
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A polynomial time computation of the exact correlation structure of k-satisfiability landscapes
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Computational Biology and Chemistry
An analysis of phase transition in NK landscapes
Journal of Artificial Intelligence Research
A multiagent evolutionary algorithm for combinatorial optimization problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A population-based local search for solving a bi-objective vehicle routing problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
NK landscapes, problem difficulty, and hybrid evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Population-based and learning-based metaheuristic algorithms for the graph coloring problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Mutation rates of the (1+1)-EA on pseudo-boolean functions of bounded epistasis
Proceedings of the 13th annual conference on Genetic and evolutionary computation
The computational complexity of N-K fitness functions
IEEE Transactions on Evolutionary Computation
Towards a population-based framework for improving stochastic local search algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Multiagent evolutionary algorithm (MEA) is a relatively new optimization technique, where a life cycle of a population of agents, which perform local search, is simulated. The algorithm was originally intended as a method for solving the graph coloring problem and incorporates ideas such as lifespans of agents and a positive or negative reinforcement for the ability of the agent to improve fitness or its stagnation. In this paper, we propose to use MEA for optimization on NK fitness landscapes. These landscapes are popular for the tunability of their ruggedness and are a particularly interesting use case for MEA. This algorithm is especially well suited for functions, where local search tends to fail because of their multimodality. However, using many short-term local search subroutines in a well-tuned version of MEA can significantly improve the results of the same local search algorithm. Experimental results are presented for MEA with the simple (1+1) Evolutionary Algorithm ((1+1) EA) used as a local search subroutine. These results show that in large and more rugged NK landscapes, MEA outperforms the multi-start (1+1) EA with number of parallel starts equal to the initial population size of MEA. This is the first time we obtained results, which clearly indicate that solely the emergent multiagent nature of MEA, driven by the lifespans and the reinforcement mechanism, is able to improve the results of multi-start local search.