Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Best-first fixed-depth minimax algorithms
Artificial Intelligence
Tabu Search
Computer Graphics with OpenGL
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Dissipative particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Particle swarm optimisation with spatial particle extension
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Rigorous analyses of simple diversity mechanisms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An Improved Particle Swarm Optimization with Mutation Based on Similarity
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Genetic programming that ensures programs are original
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Continuous non-revisiting genetic algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A study of operator and parameter choices in non-revisiting genetic algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems
Expert Systems with Applications: An International Journal
A new memory based variable-length encoding genetic algorithm for multiobjective optimization
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Analysis of (1+1) evolutionary algorithm and randomized local search with memory
Evolutionary Computation
Emergency resources scheduling based on adaptively mutate genetic algorithm
Computers in Human Behavior
A simulated annealing method based on a specialised evolutionary algorithm
Applied Soft Computing
Artificial Intelligence in Medicine
A new evolutionary search strategy for global optimization of high-dimensional problems
Information Sciences: an International Journal
An evolutionary linear programming algorithm for solving the stock reduction problem
International Journal of Computer Applications in Technology
Registrar: a complete-memory operator to enhance performance of genetic algorithms
Journal of Global Optimization
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
A space search optimization algorithm with accelerated convergence strategies
Applied Soft Computing
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A novel genetic algorithm is reported that is non-re-visiting: It remembers every position that it has searched before. An archive is used to store all the solutions that have been explored before. Different from other memory schemes in the literature, a novel binary space partitioning tree archive design is advocated. Not only is the design an efficient method to check for revisits, if any, it in itself constitutes a novel adaptive mutation operator that has no parameter. To demonstrate the power of the method, the algorithm is evaluated using 19 famous benchmark functions. The results are as follows. 1) Though it only uses finite resolution grids, when compared with a canonical genetic algorithm, a generic real-coded genetic algorithm, a canonical genetic algorithm with simple diversity mechanism, and three particle swarm optimization algorithms, it shows a significant improvement. 2) The new algorithm also shows superior performance compared to Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a state-of-the-art method for adaptive mutation. 3) It can work with problems that have large search spaces with dimensions as high as 40. 4) The corresponding CPU overhead of the binary space partitioning tree design is insignificant for applications with expensive or time-consuming fitness evaluations, and for such applications, the memory usage due to the archive is acceptable. 5) Though the adaptive mutation is parameter-less, it shows and maintains a stable good performance. However, for other algorithms we compare, the performance is highly dependent on suitable parameter settings.