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

26 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

Fingerprint

Evolutionary algorithms
Mathematical operators
Innovation

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

Cite this

@article{5ac6ae140d2b44b98aacdbfc665b4d4e,
title = "Inventive problem solving based on dialectical negation, using evolutionary algorithms and TRIZ heuristics",
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). {\circledC} 2010 Elsevier B.V. All rights reserved.",
author = "Roberto Duran-Novoa and Noel Leon-Rovira and Humberto Aguayo-Tellez and David Said",
year = "2011",
month = "5",
day = "1",
doi = "10.1016/j.compind.2010.12.006",
language = "English",
pages = "437--445",
journal = "Computers in Industry",
issn = "0166-3615",
publisher = "Elsevier",

}

Inventive problem solving based on dialectical negation, using evolutionary algorithms and TRIZ heuristics. / Duran-Novoa, Roberto; Leon-Rovira, Noel; Aguayo-Tellez, Humberto; Said, David.

In: Computers in Industry, 01.05.2011, p. 437-445.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Inventive problem solving based on dialectical negation, using evolutionary algorithms and TRIZ heuristics

AU - Duran-Novoa, Roberto

AU - Leon-Rovira, Noel

AU - Aguayo-Tellez, Humberto

AU - Said, David

PY - 2011/5/1

Y1 - 2011/5/1

N2 - 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.

AB - 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.

U2 - 10.1016/j.compind.2010.12.006

DO - 10.1016/j.compind.2010.12.006

M3 - Article

SP - 437

EP - 445

JO - Computers in Industry

JF - Computers in Industry

SN - 0166-3615

ER -