Inventive problem solving based on dialectical negation, using evolutionary algorithms and TRIZ heuristics

Roberto Duran-Novoa, Noel Leon-Rovira, Humberto Aguayo-Tellez, David Said

Resultado de la investigación

29 Citas (Scopus)

Resumen

The ability to solve inventive problems is at the core of the innovation process; however, the standard procedure to deal with them is to utilize random trial and error, despite the existence of several theories and methods. TRIZ and evolutionary algorithms (EA) have shown results that support the idea that inventiveness can be understood and developed systematically. This article presents a strategy based on dialectical negation in which both approaches converge, creating a new conceptual framework for enhancing computer-aided problem solving. Two basic ideas presented are the inversion of the traditional EA selection ("survival of the fittest"), and the incorporation of new dialectical negation operators in evolutionary algorithms based on TRIZ principles. Two case studies are the starting point to discuss what kind of results can be expected using this "Dialectical Negation Algorithm" (DNA). © 2010 Elsevier B.V. All rights reserved.
Idioma originalEnglish
Páginas (desde-hasta)437-445
Número de páginas9
PublicaciónComputers in Industry
DOI
EstadoPublished - 1 may 2011
Publicado de forma externa

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

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