Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Biases in the Crossover Landscape
Proceedings of the 3rd International Conference on Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
When Will a Genetic Algorithm Outperform Hill Climbing?
Proceedings of the 5th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Bayesian Methods for Efficient Genetic Programming
Genetic Programming and Evolvable Machines
Evolutive Introns: A Non-Costly Method of Using Introns in GP
Genetic Programming and Evolvable Machines
Toward Machine Learning Through Genetic Code-like Transformations
Genetic Programming and Evolvable Machines
Evolutionary Techniques for Minimizing Test Signals Application Time
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Modification point depth and genome growth in genetic programming
Evolutionary Computation
Gene expression and scalable genetic search
Advances in evolutionary computing
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Tantrix: A Minute to Learn, 100 (Genetic Algorithm) Generations to Master
Genetic Programming and Evolvable Machines
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Variable Length Representation in Evolutionary Electronics
Evolutionary Computation
Analyzing the effects of module encapsulation on search space bias
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A comparison of the fixed and floating building block representation in the genetic algorithm
Evolutionary Computation
Implicit representation in genetic algorithms using redundancy
Evolutionary Computation
Collective adaptation: The exchange of coding segments
Evolutionary Computation
Putting more genetics into genetic algorithms
Evolutionary Computation
The benefits of computing with introns
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A survey on chromosomal structures and operators for exploiting topological linkages of genes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Duplication of coding segments in genetic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Positional independence and recombination in cartesian genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
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The genetic algorithm (GA) is a problem-solving method that is modeled after the process of natural selection. We are interested in studying a specific aspect of the GA: the effect of noncoding segments on GA performance. Noncoding segments are segments of bits in an individual that provide no contribution, positive or negative, to the fitness of that individual. Previous research on noncoding segments suggests that including these structures in the GA may improve GA performance. Understanding when and why this improvement occurs will help us to use the GA to its full potential. In this article we discuss our hypotheses on noncoding segments and describe the results of our experiments. The experiments may be separated into two categories: testing our program on problems from previous related studies, and testing new hypotheses on the effect of noncoding segments.