Name
Abstract | ||
|---|---|---|
The importance of fields of knowledge like Biology, Psychology, and Social Sciences as sources of inspiration for Computational Intelligence has been increasing in the last years, deeply influencing Evolutionary Computation and its applications, inspiring the development of algorithms and methodologies like evolutionary programming and particle swarm optimization. However, the proliferation of biologically-inspired algorithms and solutions indicates the actual focus of researchers and, consequently, Philosophy is still faced as a sort of obscure and enigmatic knowledge, despite the power of generalization and the systematic nature of philosophical investigative methods like dialectics. This work proposes an evolutionary class of algorithms based on the materialist dialectics, namely the Objective Dialectical Method, to be used in search and optimization problems. To validate our proposal we developed sim ulations using several benchmarks functions. The generated results were evaluated in minimization problems concerning how near the results are from the minimum value and how many iterations were used until the estimated minimum value reached a specific threshold value set as a determined precision. This work showed that the proposed dialectical algorithm has good performance in global optimization. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IJCNN.2009.5178738 | IJCNN |
Keywords | Field | DocType |
particle swarm optimization,evolutionary class,computational intelligence,specific threshold value,minimum value,optimization problem,global optimization,evolutionary programming,enigmatic knowledge,estimated minimum value,evolutionary computation,correlation,evolutionary computing,systematics,probability density function,data mining,minimisation,genetic programming,benchmark testing,social sciences,computational biology,psychology,search problem,optimization,force,social science,biology | Particle swarm optimization,Computational intelligence,Global optimization,Computer science,sort,Evolutionary computation,Artificial intelligence,Search problem,Evolutionary programming,Optimization problem,Machine learning | Conference |
ISSN | ISBN | Citations |
2161-4393 | 978-1-4244-3553-1 | 1 |
PageRank | References | Authors |
0.39 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wellington P. dos Santos | 1 | 36 | 11.00 |
| Francisco M. De Assis | 2 | 10 | 5.15 |
| dos Santos, W.P. | 3 | 1 | 0.39 |
| de Assis, F.M. | 4 | 4 | 3.20 |