Neural network methods in combinatorial optimization
Computers and Operations Research - Special issue on neural networks and operations research
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Algorithms and Highly Constrained Problems: The Time-Table Case
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A genetic algorithm approach to multiobjective land use planning
Computers and Operations Research
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Automated innovization for simultaneous discovery of multiple rules in bi-objective problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
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The inadequacy of classical methods to handle resource allocation problems (RAPs) draw the attention of evolutionary algorithms (EAs) to these problems. The potentialities of EAs are exploited in the present work for handling two such RAPs of quite different natures, namely (1) university class timetabling problem and (2) land-use management problem. In many cases, these problems are over-simplified by ignoring many important aspects, such as different types of constraints and multiple objective functions. In the present work, two EA-based multi-objective optimizers are developed for handling these two problems by considering various aspects that are common to most of their variants. Finally, the similarities between the problems, and also between their solution techniques, are analyzed through the application of the developed optimizers on two real problems.