1f1f598314
The original tutorial link[http://paradiseo.gforge.inria.fr/index.php?n=Doc.Tutorials] is broken. I found the archive from here: https://web.archive.org/web/20210119160107/http://paradiseo.gforge.inria.fr/index.php?n=Doc.Tutorials
60 lines
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2.2 KiB
Markdown
60 lines
No EOL
2.2 KiB
Markdown
# How to hybrid an evolutionary algorithm and a local search
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In this lesson, a hybridization between an evolutionary algorithm(EA) and a local search is presented. It will be illustrated by an example on the Queen problem. Here, the hybridization consists in replacing the mutation operator of the EA by a first improvement hill climber.
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1. hybridization
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2. Exercise
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## 1. Hybridization (example on the Queen problem)
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First, you have to define the represenation of a Queen, how to initialize and how to evaluate a population of solutions:
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```c++
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queenFullEval<Queen> fullEval;
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eoInitPermutation<Queen> init(vecSize);
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eoPop<Queen> pop;
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Queen tmp;
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for(unsigned int i=0; i<20; i++){ //population size is fixed to 20
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init(tmp);
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fullEval(tmp);
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pop.push_back(tmp);
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}
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```
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As in previous lessons, a local search is declared (first improvement hill climber):
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```c++
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moFullEvalByCopy<shiftNeighbor> shiftEval(fullEval);
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orderShiftNeighborhood orderShiftNH(pow(vecSize-1, 2));
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moFirstImprHC<shiftNeighbor> hc(orderShiftNH, fullEval, shiftEval);
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```
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To hybrid this local search with an EA, you just have to use it instead of a classical mutation:
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```c++
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eoOrderXover<Queen> cross;
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eoSGATransform<Queen> transform(cross, 0.3, hc, 0.7); // cross and mutation probabilities are fixed
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```
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Others components of the "eoEasyEA" have to be declared:
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```c++
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eoGenContinue<Queen> EAcont(50); //nb generations is fixed to 50
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eoDetTournamentSelect<Queen> selectOne(2); //size of tournament is fixed to 2
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eoSelectMany<Queen> select(selectOne, 1); //rate of selection is fixed to 1
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eoGenerationalReplacement<Queen> repl;
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```
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More details are available in EO lessons.
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Finally, the hybrid algorithm is declared as:
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```c++
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eoEasyEA<Queen> hybridAlgo(EAcont, fullEval, select, transform, repl);
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```
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and should be applied on the population with:
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```c++
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hybridAlgo(pop);
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```
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You can test this hybrid algorithm by changing problem size (use parameters file or the option --vecSize=X on command line to execute "hybridAlgo"). It prints the initial and final population.
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## 2. Exercise
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Try to use a hybridization at the checkpointing step rather than at the mutation step. You have to implement an "eoUpdater" which applies a local search. This updater should be added in a "eoCheckpoint". |