Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Comparison between lamarckian and baldwinian repair on multiobjective 0/1 knapsack problems
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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Nutrition is one of many disciplines, where intractable optimization problems naturally arise and require special attention of meta-heuristic methods. The problem of generating an optimal dietary menu that considers nutrient and non-nutrient requirements can be solved by evolutionary algorithms in an efficient and effective way. The menu optimality may be defined by several factors, such as cost, food functionality and season, as well as aesthetic standards for taste, consistency, color, temperature, shape, and method of preparation. In this paper, we present a multi-objective and multi-constrained evolutionary algorithm for planning and optimization of daily menus. It is based on the Elitist Non-Dominated Sorting Genetic Algorithm [2] and the Baldwinian repair algorithm [6]. The paper also presents an example of planning a daily menu by this method to demonstrate the approach.