A genetic algorithm tutorial

Statistics and Computing

Volumn 4, Issue 2, 1994, Pages 65-85

  Whitley, Darrell ^a  

^a Veterinary Dentistry and Oral Surgery   (United States)

Author keywords

Genetic algorithms; parallel algorithms; search

Indexed keywords

EID: 0002338687     PISSN: 09603174     EISSN: 15731375     Source Type: Journal    
DOI: 10.1007/BF00175354     Document Type: Article

References (55)

1. A new interpretation of the schema notation that overturns the binary encoding constraint
2. A survey of evolution strategies
3. Adaptive selection methods for genetic algorithms
4. Improving search in genetic algorithms
5. Selection in massively parallel genetic algorithms
6. A naturally occurring niche and species phenomenon: the model and first results
7. The CHC adaptive search algorithm
8. Genetic algorithms in noisy environments
9. Simple genetic algorithms and the minimal, deceptive problem
10. The theory of virtual alphabets
11. An analysis of a reordering operator on a GA-hard problem
12. A comparative analysis of selection schemes used in genetic algorithms
13. Explicit parallelism of genetic algorithms through population structures
14. Optimization of control parameters for genetic algorithms
15. Deception considered harmful
16. How genetic algorithms work: a critical look at implicit parallelism
17. Co-evolving parasites improve simulated evolution as an optimizing procedure
18. Representation issues in genetic algorithms
19. Fine grained parallel genetic algorithms
20. Evolution in time and space—the parallel genetic algorithm
21. How genetic algorithms really work: I. Mutation and hillclimbing
22. Modelling genetic algorithms with Markov chains
23. Some effects of selection procedures on hyperplane sampling by genetic algorithms
24. Real-coded genetic algorithms and interval schemata
25. An analysis of multi-point crossover
26. Uniform crossover in genetic algorithms
27. A study of reproduction in generational and steady-state genetic algorithms
28. Optimization using distributed genetic algorithms
29. Distributed genetic algorithms
30. Modeling simple genetic algorithms
31. Punctuated equilibria in genetic search
32. The GENITOR algorithm and selective pressure
33. Fundamental principles of deception in genetic search
34. An executable model of a simple genetic algorithm
35. Cellular genetic algorithms
36. Genitor II: a distributed genetic algorithm
37. Tracking primary hyperplane competitors during genetic search
38. Genetic algorithms for real parameter optimization