Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Data Structures and Algorithms
Data Structures and Algorithms
Combining Control Strategies Using Genetic Algorithms with Memory
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
An evolutionary algorithm with solution archive for the generalized minimum spanning tree problem
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Genetic algorithms (GAs) share a common weakness with most other metaheuristics: Candidate solutions are in general revisited multiple times, lowering diversity and wasting precious CPU time. We propose a complete solution archive based on a special binary trie structure for GAs with binary representations that efficiently stores all evaluated solutions during the heuristic search. Solutions that would later be revisited are detected and effectively transformed into similar yet unconsidered candidate solutions. The archive's relevant insert, find, and transform operations all run in time O(l) where l is the length of the solution representation. From a theoretical point of view, the archive turns the GA into a complete algorithm with a clear termination condition and bounded run time. Computational results are presented for Royal Road functions and NK landscapes, indicating the practical advantages.