Evolving color recipes

Eiji Mizutani, Hideyuki Takagi, David M. Auslander, Jyh Shing Roger Jang

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


This paper highlights an evolutionary computing intelligence for a computerized color recipe prediction that requires function approximation and combinatorial solution of colorants to produce color recipes for a given target color sample. We attack this real challenging problem in the color (paint) industry by using an evolutionary computing system that consists of a problem-specific knowledge and three principal constituents of soft-computing: neural networks, a fuzzy system, and a genetic algorithm. Departing from the recipe results obtained by neural networks (NN) approaches, the evolutionary system attempts to improve them in conjunction with fuzzy classification, a knowledge base and neural fitness functions. All components function synergistically in obtaining precise color recipe outputs through simulation of color paint manufacturing process. Such computational intelligence can be useful, especially when an exact mathematical model of the real-world process under consideration is not available explicitly.

Original languageEnglish
Pages (from-to)537-550
Number of pages14
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number4
Publication statusPublished - Nov 2000

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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