CscStrategyType.h

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/*
 * CscStrategyType.h
 *
 *  Created on: 31 mai 2014
 *      Author: ilieszaoui
 */
 
#ifndef CSCSTRATEGYTYPE_H_
#define CSCSTRATEGYTYPE_H_
 
enum CscStrategyType {
    //Random Strategy
        /*
         strategy consisting of creating a sequence fully randomly
         */
        Random,
    // Reproduction strategies
        /*
         strategy consisting of copying a sequence of a parent selected by tournament
         */
        PerfectCloning,
        /*
         strategy consisting of copying the best parent's sequence of the previous generation
         */
        PerfectCloningTheBest,
    // Stationary strategy
        /*
         strategy consisting of creating a sequence of only one state, that is the initial state
         */
        Stationary,
    // InverseKinematic strategy
        InverseKinematic,
    // perfectionning strategies
        /*
         strategy consisting of copying a sequence of a parent selected by tournament until state having best objective value,
         then add random state until max sequence size
         */
        RandomFromBest, PerfectionningCloning,
        /*
        strategy consisting of copying a sequence of a parent selected by tournament until state having best objective value,
        then add random symetric state until max sequence size
        */
        RandomSymetryFromBest,
        /*
        strategy consisting of copying a sequence of a parent selected by tournament until state having big drop in objectiveValue,
        then add states created with inverseKinematic until max sequence size
        */
        RandomWithIKFromFirstBigDrop,
        /*
        strategy consisting of make polynomial regression about a relationship between a data and objectiveValue with all the previous generation,
        then create a new sequence with the optimal value according to this regression
        */
        GenerationalLearningAndOptimization,
        /*
        strategy consisting of make polynomial regression about a relationship between a data and objectiveValue with a randomly selected portion of the previous generation,
        then create a new sequence with the optimal value according to this regression
        */
        PartialGenerationalLearningAndOptimization,
    // Crossing strategies
        /*
         strategy consisting of copying a sequence of a first parent selected by tournament from first state until randomly selected state (N state)
         and then completing sequence whith a sequence of a second parent selected by tournament from N state to final state
         */
        NCrossing,
        /*
         strategy consisting of copying the all sequence of a first parent selected by tournament BUT only for selected members,
         others members are copied from a second parent selected by tournament
         */
        HorizontalNCrossing,
        /*
         strategy consisting of copying a sequence of a first parent selected by tournament from first state until randomly selected state (N state)
         and then completing sequence with a sequence of a second parent selected by tournament from N state to final state BUT only for selected members,
         others members are copied from the first parent
         */
        HorizontalNCrossingFromNState,
        /*
         same strategy as HorizontalNCrossingFromNState BUT the NState is randomly selected between state 0 and state having best objective value of first parent
         */
        HorizontalNCrossingUntilBestState,
        /*
        strategy consisting of copying right side sequence of a first parent selected by tournament
        and then completing sequence whith a left side sequence of a second parent selected by tournament
        */
        RightLeftCrossover,
    // Mutation strategies
        /*
         strategy consisting of copying a sequence of a parent selected by tournament
         and modifying only one randomly selected state with random values
         */
        StateMutation,
        /*
         strategy consisting of copying a sequence of a parent selected by tournament
         and modifying the value of one randomly selected output for each sequence's states
         */
        OneOutputMutation,
        /*
         strategy consisting of copying a sequence of a parent selected by tournament
         and modifying the value of one randomly selected member of only one randomly selected state
         */
        OutputFromNStateMutation,
        /*
         strategy consisting of copying a sequence of a parent selected by tournament
         and each left side output take this right one equivalent output or conversely
         */
        SymetryRightLeftMutation,
    
        None
    
};
 
#endif /* CSCSTRATEGYTYPE_H_ */