Global optimization
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Adaptive global optimization with local search
Adaptive global optimization with local search
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Reconsidering the progress rate theory for evolution strategies in finite dimensions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Balancing Population- and Individual-Level Adaptation in Changing Environments
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Special issue on emerging trends in soft computing: memetic algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A study of the Lamarckian evolution of recurrent neural networks
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coevolving Memetic Algorithms: A Review and Progress Report
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Memetic Algorithm for VLSI Floorplanning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Balancing Population- and Individual-Level Adaptation in Changing Environments
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Towards a memetic feature selection paradigm
IEEE Computational Intelligence Magazine
Memetic compact differential evolution for cartesian robot control
IEEE Computational Intelligence Magazine
Natural and remote sensing image segmentation using memetic computing
IEEE Computational Intelligence Magazine
Chemical-reaction-inspired metaheuristic for optimization
IEEE Transactions on Evolutionary Computation
Feasibility structure modeling: an effective chaperone for constrained memetic algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Disturbed Exploitation compact Differential Evolution for limited memory optimization problems
Information Sciences: an International Journal
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Imitation tendencies of local search schemes in baldwinian evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Differential evolution with self adaptive local search
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Parallel island-based multiobjectivised memetic algorithms for a 2D packing problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A novel approach for automated music composition using memetic algorithms
Proceedings of the 49th Annual Southeast Regional Conference
Evaluation of two-stage ensemble evolutionary algorithm for numerical optimization
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Ockham's Razor in memetic computing: Three stage optimal memetic exploration
Information Sciences: an International Journal
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
Using computational intelligence for large scale air route networks design
Applied Soft Computing
Memetic algorithms for de novo motif-finding in biomedical sequences
Artificial Intelligence in Medicine
Information Sciences: an International Journal
Two-stage ensemble memetic algorithm: Function optimization and digital IIR filter design
Information Sciences: an International Journal
Computers & Mathematics with Applications
Information Sciences: an International Journal
Enhancing the performance of hybrid genetic algorithms by differential improvement
Computers and Operations Research
Multi-modal valley-adaptive memetic algorithm for efficient discovery of first-order saddle points
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming
Expert Systems with Applications: An International Journal
Memetic algorithm based task scheduling using probabilistic local search
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
A memetic approach to bayesian network structure learning
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
An intelligent multi-restart memetic algorithm for box constrained global optimisation
Evolutionary Computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Memetic algorithms (MAs) represent one of the recent growing areas in evolutionary algorithm (EA) research. The term MAs is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian EAs, Lamarckian EAs, cultural algorithms, or genetic local searches. In the last decade, MAs have been demonstrated to converge to high-quality solutions more efficiently than their conventional counterparts on a wide range of real-world problems. Despite the success and surge in interests on MAs, many of the successful MAs reported have been crafted to suit problems in very specific domains. Given the restricted theoretical knowledge available in the field of MAs and the limited progress made on formal MA frameworks, we present a novel probabilistic memetic framework that models MAs as a process involving the decision of embracing the separate actions of evolution or individual learning and analyzing the probability of each process in locating the global optimum. Further, the framework balances evolution and individual learning by governing the learning intensity of each individual according to the theoretical upper bound derived while the search progresses. Theoretical and empirical studies on representative benchmark problems commonly used in the literature are presented to demonstrate the characteristics and efficacies of the probabilistic memetic framework. Further, comparisons to recent state-of-the-art evolutionary algorithms, memetic algorithms, and hybrid evolutionary-local search demonstrate that the proposed framework yields robust and improved search performance.