An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Invention and creativity in automated design by means of genetic programming
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Innovization: innovating design principles through optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Development of an engine crankshaft in a framework of computer-aided innovation
Computers in Industry
A new design optimization framework based on immune algorithm and Taguchi's method
Computers in Industry
The virtual gene genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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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).