A branch-and-cut algorithm for nonconvex quadratic programs by Vandenbussche D., Nemhauser G. L.

By Vandenbussche D., Nemhauser G. L.

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Extra resources for A branch-and-cut algorithm for nonconvex quadratic programs with box constraints

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Modified operators One approach to alleviating the difficulties encountered by the standard operators is to design operators that are appropriate for the problem. 5) is a typical example. Modified formulation Another approach that has proved fruitful in some cases is to modify the problem formulation. Reeves [219] found good solutions to bin-packing problems by reformulating the problem.

Overlapping populations take this a stage further by replacing only a fraction G (the generation gap) of the population at each generation. Finally, taking this to its logical conclusion produces the so-called steady-state or incremental strategies, in which only one new chromosome (or sometimes a pair) is generated at each stage. Davis [47] gives a good general introduction to this type of GA. Slightly different strategies are commonly used in the ES community, which traditionally designates them either (Note that 46 CHAPTER 2.

To obtain search space coverage at this level with simple random initialization would need a much larger population. Some other possibilities for GAs are discussed by Kallel and Schoenauer [139]. More widely, it should be noted that in OR the value of using ‘low discrepancy sequences’ instead of pseudorandom ones is being increasingly recognized. L’Ecuyer and Lemieux [149] give a recent survey; whether such techniques have a place in evolutionary algorithms is so far unexplored. A final point to mention here is the possibility of seeding the initial population with known good solutions.

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