Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Information Processing and Management: an International Journal
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
The Complexity of Some Problems on Subsequences and Supersequences
Journal of the ACM (JACM)
Evolution using genetic programming of a low-distortion, 96 decibel operational amplifier
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Flexible pattern matching in strings: practical on-line search algorithms for texts and biological sequences
Two Algorithms for the Longest Common Subsequence of Three (or More) Strings
CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
A Survey of Longest Common Subsequence Algorithms
SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
A genetic algorithm for the longest common subsequence problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Automated synthesis of analog electrical circuits by means ofgenetic programming
IEEE Transactions on Evolutionary Computation
Analysis of evolutionary algorithms for the longest common subsequence problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Simulated annealing, its parameter settings and the longest common subsequence problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A hybrid metaheuristic for the longest common subsequence problem
HM'10 Proceedings of the 7th international conference on Hybrid metaheuristics
Computing longest common subsequences with the B-cell algorithm
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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An evolutionary algorithm (EA) usually initializes its population with random genotypes, which represent random solutions to the target problem instance. If the problem is one of constrained optimization, an initial population whose genotypes all represent empty solutions might allow the EA to grow valid solutions as much as search for them and thereby identify good solutions more quickly. This is the case in a genetic algorithm (GA) for the longest common subsequence problem, which seeks the length of a longest subsequence common to each of a set of given strings. The GA encodes sequences as binary strings that indicate subsequences of the shortest or first given string. In tests on a variety of problem instances, the GA always identifies an optimum subsequence, but on most instances, the GA reaches an optimum more quickly when its initial population encodes empty sequences than when its initial genotypes represent random sequences.