Effective backpropagation training with variable stepsize
Neural Networks
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Exponentially Many Steps for Finding a Nash Equilibrium in a Bimatrix Game
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Settling the Complexity of Two-Player Nash Equilibrium
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
A constraint handling approach for the differential evolution algorithm
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
On the Complexity of Nash Equilibria and Other Fixed Points (Extended Abstract)
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Discrete differential evolution algorithm for the job shop scheduling problem
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Novel Binary Differential Evolution Algorithm for Discrete Optimization
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 04
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Differential Evolution (DE) is a simple and powerful optimization method, which is mainly applied to numerical optimization. In this article we present a new selective mutation operator for the Differential Evolution. We adapt the Differential Evolution algorithm to the problem of finding the approximate Nash equilibrium in n person games in the strategic form. Finding the Nash equilibrium may be classified as continuous problem, where two probability distributions over the set of pure strategies of both players should be found. Every deviation from the global optimum is interpreted as the Nash approximation and called ε-Nash equilibrium. The fitness function in this approach is based on the max function which selects the maximal value from the set of payoffs. Every element of this set is calculated on the basis of the corresponding genotype part. We propose an approach, which allows us to modify only the worst part of the genotype. Mainly, it allows to decrease computation time and slightly improve the results.