EVOLVE
Version
Jan 17, 1993
Description
EVOLVE is a genetic search algorithm that can be used to optimize problems with a mix of continuous, integer and discrete design variables. Constraints are considered by using an exterior penalty function. It has a capability to improve global search (called sharing) by penalizing designs that are near to other good designs.
References
EVOLVE Manual
Control Parameters
| Name | Default Value | Description |
|---|---|---|
| Crossover | ||
| CrossoverRate | 0.8 | Probability of crossover. This is typically between 0.6 and 0.9 and valid range is [0, 1]. |
| DirectedCrossover | 0 | Generation at which directed crossover is to be started. 0 disables directed crossover. Option accepts positive integer values. |
| DirectedCrossoverGenerations | 1 | Number of previous generations over which information of positional gain will be used to direct the crossover. Use only if DirectedCrossover > 1. Option must have an integer value greater than 0. |
| Optimization Parameters | ||
| Accuracy | 0.01 | Precision level of the continuous variables. Indicates the number of discrete elements the value range is divided by |
| Details | No | Enables accuracy specification for every continuous variable |
| DefaultAccuracy | 0.01 | Common accuracy value if detailed specification is disabled |
| AccuracyIncrease | 0.2 | Accuracy change factor by which accuracy is multiplied when passing to next stage. Provides a means to increase accuracy in subsequent stages. Thus should have a value greater than 0. |
| Elite | 1 | Number of elitist designs to be kept (1-5) while moving to the next generation. Option must have an integer value strictly greater than 0 and less than the PopulationSize. |
| GrayCode | No | Enables gray code binary implementation. If disabled, fixed point binary representation is used. |
| MutationRate | 0.01 | Probability of mutation for each bit. Typically 0.001 to 0.1 and valid range is between 0 and 1. |
| PopulationSize | 60 | Size of the population for algorithm to begin with. Option must have integer value greater than or equal to 20. |
| RevivalChance | 0 | Chance of revival of lost bits (0.0001 . 0.002). The valid range for the option is between 0 and 1. |
| Seed | Random seed (optional). This option is optional but specifying a valid integer seed value could help generate the same random numbers and thus have same populations between 2 different algorithm runs provided all other parameters stay the same. | |
| WindowSize | 3 | The worst values of pseudo objective are selected from given number of previous generation. The option must have a positive integer value. |
| Output | ||
| Clusters | 1 | Number of clusters to be formed by the designs of the last generation and saved to clusters.dat file. Clustering will have better effect if used along with sharing. 1 for regular output (without clustering). Option must have a positive integer value less than PopulationSize. |
| OutputFilesPath | Path where output files are to be generated. Defaults to user's temporary directory if not specified | |
| PrintDetails | Yes | Enables printing details for each generation |
| PrintOutFrequency | 60 | Printing frequency control. Number of evaluations after which data is written to the history.dat file. Option must have a positive integer value. |
| Penalty Function | ||
| MaxCap | 7 | Initial maximum cap on the penalty of constraint violations. Any penalty which exceeds this maximum cap will be adjusted to the sum of the maximum cap and 20% of the amount of penalty over the value of MaxCap. Option must have a value greater than 0. |
| PenaltyAdjuster | 1 | Penalty adjuster which is added to penalty coefficient after every 10 generations. Option must have a value greater than or equal to 0. |
| PenaltyCoefficient | 2 | Starting penalty coefficient for constraint violations. Option must have a positive value. |
| Sharing Strategy | ||
| Alpha | 1 | Alpha parameter defines the nature of sharing function |
| Share | No | Enables sharing strategy enhancing the ability of location global minimum |
| SigmaShare | 0.2 | Sigma parameter defines the radius of sharing neighborhood. Option must have a value greater than 0. |
| Termination Criterion | ||
| ConvergenceIndex | 0.9 | Convergence criterion index. If the portion of bits that have identical zeros or ones for all designs is over this index, the algorithm stops. The option must have a value between 0 and 1. |
| MaxEvaluations | 1500 | Maximum number of function evaluations per stage. Option must have a positive integer value. |
| Varying Granularity | ||
| MeltingDesigns | 40 | Number of designs from previous generation to form initial population using melting strategy. Option must have a positive integer value less than the PopulationSize. |
| Relaxation | Percent values of constraints relaxations for varying granularity approach | |
| Stage 1 | 40 | constraint relaxation for stage 1 |
| Stage 2 | 20 | constraint relaxation for stage 2 |
| Stage 3 | 0 | constraint relaxation for stage 3 |
| Stage 4 | 0 | constraint relaxation for stage 4 |
| Stage 5 | 0 | constraint relaxation for stage 5 |
| Stages | 3 | Number of stages for varying granularity approach. Up to 5 stages allowed. Option must have an integer value between 1 and 5. |