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Syracuse University

Genetic algorithms

Electrical Engineering and Computer Science - All Scholarship

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Full-Text Articles in Physical Sciences and Mathematics

Adaptive Linkage Crossover, Ayed A. Salman, Kishan Mehrotra, Chilukuri K. Mohan Jan 1998

Adaptive Linkage Crossover, Ayed A. Salman, Kishan Mehrotra, Chilukuri K. Mohan

Electrical Engineering and Computer Science - All Scholarship

Problem-specific knowledge is often implemented in search algorithms using heuristics to determine which search paths are to be explored at any given instant. As in other search methods, utilizing this knowledge will more quickly lead a genetic algorithm (GA) towards better results. In many problems, crucial knowledge is not found in individual components, but in the interrelations between those components. For such problems, we develop an interrelation (linkage) based crossover operator that has the advantage of liberating GAs from the constraints imposed by the fixed representations generally chosen for problems. The strength of linkages between components of a chromosomal structure …


Partial Shape Matching Using Genetic Algorithms, Ender Ozcan, Chilukuri K. Mohan Jan 1998

Partial Shape Matching Using Genetic Algorithms, Ender Ozcan, Chilukuri K. Mohan

Electrical Engineering and Computer Science - All Scholarship

Shape recognition is a challenging task when images contain overlapping, noisy, occluded, partial shapes. This paper addresses the task of matching input shapes with model shapes described in terms of features such as line segments and angles. The quality of matching is gauged using a measure derived from attributed shape grammars. We apply genetic algorithms to the partial shape-matching task. Preliminary results, using model shapes with 6 to 70 features each, are extremely encouraging.


Simulated Annealing And Genetic Algorithms For Partial Shape Matching, Ender Ozcan, Chilukuri K. Mohan Jan 1997

Simulated Annealing And Genetic Algorithms For Partial Shape Matching, Ender Ozcan, Chilukuri K. Mohan

Electrical Engineering and Computer Science - All Scholarship

Partial shape matching may be viewed as an optimization problem, to be solved using methods such as simulated annealing (SA) and genetic algorithms (GAs). We apply and compare both these methods for matching input shapes with model shapes described in terms of features such as line segments and angles. The quality of matching is gauged using a measure derived from attributed shape grammars [10, 11]. Current results show that both SA and GA succeed in the shape matching task; the GA is faster and yields the global optimum more often than the versions of SA implemented.