`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Self-adaptive multimethod search for global optimization in real-parameter spaces
IEEE Transactions on Evolutionary Computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Population-based algorithm portfolios for numerical optimization
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
Handbook of Memetic Algorithms
Handbook of Memetic Algorithms
Ockham's Razor in memetic computing: Three stage optimal memetic exploration
Information Sciences: an International Journal
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization
IEEE Transactions on Evolutionary Computation
Compact Differential Evolution
IEEE Transactions on Evolutionary Computation
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An extremely natural yet efficient design pattern in memetic computing optimisation is the sequential structure algorithms composed of few simple memes executed sequentially, each one with its own specific role, have proven to be robust and versatile on various optimisation problems with diverse features and dimensionality values. This principle of non-complexity, which can be seen as an application of the Ockham's Razor in memetic computing, leads us to create shrinking three-stage optimal memetic exploration S-3SOME, a scheme which progressively perturbs a candidate solution by alternating three search operators, the first one being a stochastic global search, the second a random sampling within progressive narrowing hyper-volume, and the third a deterministic local search. Numerical results show that the proposed S-3SOME, despite its simplicity, is competitive not only with other memory-saving schemes recently proposed in literature, but also with complex state-of-the-art population-based algorithms characterised by high computational overhead and memory employment.