Dynamic Programming
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
Valley-Adaptive Clearing Scheme for Multimodal Optimization Evolutionary Search
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Towards a memetic feature selection paradigm
IEEE Computational Intelligence Magazine
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
A Multi-Facet Survey on Memetic Computation
IEEE Transactions on Evolutionary Computation
Experiences on memetic computation for locating transition states in biochemical applications
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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
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With enormous success in both science and engineering, the recent advances in evolutionary computation-particularly memetic computing-is gaining increasing attention in the molecular optimization community. In this paper, our interest is to introduce a memetic computational methodology for the discovery of low-energy stable conformations-also known as the stereoisomers-of covalently-bonded molecules, due to the abundance of such molecules in nature and their importance in biology and chemistry. To an optimization algorithm, maintaining the same set of bonds over the course of searching for the stereoisomers is a great challenge. Avoiding the steric effect, i.e. preventing atoms from overlapping or getting too close to each other, is another challenge of molecular optimization. Addressing these challenges, three novel nature-inspired tree-based evolutionary operators are first introduced in this paper. A tree-structured covalent-bond-driven molecular memetic algorithm (TCM-MA)-tailored specifically to deal with molecules that involve covalent bonding but contain no cyclic structures using the three novel evolutionary operators-is then proposed for the efficient search of the stereoisomers of ring-deficient covalently-bonded molecules. Through empirical study using the glutamic acid as a sample molecule of interest, it is witnessed that the proposed TCM-MA discovered as many as up to sixteen times more stereoisomers within as little as up to a five times tighter computational budget compared to two other state-of-the-art algorithms.