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

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

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)437-445
Number of pages9
JournalComputers in Industry
DOIs
Publication statusPublished - 1 May 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

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