Mean square rates of convergence in the continuous time simulated annealing algorithm on Rd
Advances in Applied Mathematics
Toward an extrapolation of the simulated annealing convergence theory onto the simple genetic algorithm144438
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Genetic Algorithms for Pattern Recognition
Genetic Algorithms for Pattern Recognition
Genetic Learning for Adaptive Image Segmentation
Genetic Learning for Adaptive Image Segmentation
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Map Segmentation by Colour Cube Genetic K-Mean Clustering
ECDL '00 Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
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Neoteny, also spelled Paedomorphosis, can be defined in biological terms as the retention by an organism of juvenile or even larval traits into later life. In some species, all morphological development is retarded; the organism is juvenilized but sexually mature. Such shifts of reproductive capability would appear to have adaptive significance to organisms that exhibit it. In terms of evolutionary theory, the process of paedomorphosis suggests that larval stages and developmental phases of existing organisms may give rise, under certain circumstances, to wholly new organisms. Although the present work does not pretend to model or simulate the biological details of such a concept in any way, these ideas were incorporated by a rather simple abstract computational strategy, in order to allow (if possible) for faster convergence into simple nonmemetic Genetic Algorithms, i.e. without using local improvement procedures (e.g. via Baldwin or Lamarckian learning). As a case-study, the Genetic Algorithm was used for colour image segmentation purposes by using K-mean unsupervised clustering methods, namely for guiding the evolutionary algorithm in his search for finding the optimal or sub-optimal data partition. Average results suggest that the use of neotonic strategies by employing juvenile genotypes into the later generations and the use of linear-dynamic mutation rates instead of constant, can increase fitness values by 58% comparing to classical Genetic Algorithms, independently from the starting population characteristics on the search space.