Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Evolving robot behavior via interactive evolutionary computation: from real-world to simulation
Proceedings of the 2001 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Applied Intelligence
Learning Probability Distributions in Continuous Evolutionary Algorithms - a Comparative Review
Natural Computing: an international journal
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem
IEEE Transactions on Evolutionary Computation
Bio-inspired combinatorial optimization: notes on reactive and proactive interaction
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
An experimental comparative study for interactive evolutionary computation problems
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Interactive evolutionary computation framework and the on-chance operator for product design
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
| Hi-index | 0.00 |
Evolutionary Computation encompasses computational models that follow a biological evolution metaphor. The success of these techniques is based on the maintenance of the genetic diversity for which it is necessary to work with large populations. However, it is not always possible to deal with such large populations for instance when the adequacy values must be estimated by a human being (Interactive Evolutionary Computation. IEC). This work introduces a new algorithm which is able to perform very well with a very low number of individuals (micropopulations) which speeds up the convergence and it is solving problems with complex evaluation functions. The new algorithm is compared with the canonical genetic algorithm in order to validate its efficiency. Two experimental frameworks have been chosen: table and logotype designs. An objective evaluation measures has been proposed to avoid user interaction in the experiments. In both cases the results show the efficiency of the new algorithm in terms of quality of solutions and convergence speed two key issues in decreasing user fatigue.