On heuristics as a fundamental constituent of soft computing
Fuzzy Sets and Systems
Knowledge-based genetic algorithm for university course timetabling problems
International Journal of Knowledge-based and Intelligent Engineering Systems
Computers and Operations Research
A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Evolutionary Computation
Expert Systems with Applications: An International Journal
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IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
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Bio-inspired combinatorial optimization: notes on reactive and proactive interaction
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Expert Systems with Applications: An International Journal
Symbiotic simulation for optimisation of tool operations in semiconductor manufacturing
Proceedings of the Winter Simulation Conference
Region based memetic algorithm for real-parameter optimisation
Information Sciences: an International Journal
Pheromone trail initialization with local optimal solutions in ant colony optimization
International Journal of Knowledge-based and Intelligent Engineering Systems
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We discuss implicit and explicit knowledge representation mechanisms for evolutionary algorithms (EAs). We also describe offline and online metaheuristics as examples of explicit methods to leverage this knowledge. We illustrate the benefits of this approach with four real-world applications. The first application is automated insurance underwriting-a discrete classification problem, which requires a careful tradeoff between the percentage of insurance applications handled by the classifier and its classification accuracy. The second application is flexible design and manufacturing-a combinatorial assignment problem, where we optimize design and manufacturing assignments with respect to time and cost of design and manufacturing for a given product. Both problems use metaheuristics as a way to encode domain knowledge. In the first application, the EA is used at the metalevel, while in the second application, the EA is the object-level problem solver. In both cases, the EAs use a single-valued fitness function that represents the required tradeoffs. The third application is a lamp spectrum optimization that is formulated as a multiobjective optimization problem. Using domain customized mutation operators, we obtain a well-sampled Pareto front showing all the nondominated solutions. The fourth application describes a scheduling problem for the maintenance tasks of a constellation of 25 low earth orbit satellites. The domain knowledge in this application is embedded in the design of a structured chromosome, a collection of time-value transformations to reflect static constraints, and a time-dependent penalty function to prevent schedule collisions.