Title:
Multi-objective-genetic-algorithm
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Description: Real world problems often present multiple, frequently conflicting, objectives.
The research for optimal solutions of multi-objective problems
can be achieved through means of genetic algorithms, which are inspired
by the natural process of evolution: an initial population of solutions is
randomly generated, then pairs of solutions are selected and combined in
order to create new solutions slightly different the initial solutions.
The fittest solutions are kept in the population and are used to generate
new solutions
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