The evolutionary steps of genetic programming are as follows:
1. Randomly create an initial population (generation 0) of individual computer programs composed of the available functions and terminals.
2. Iteratively perform the following sub-steps (called a generation) on the population until the termination criterion is satisfied:
a. Execute each program in the population and ascertain its fitness (explicitly or implicitly) using the problem’s fitness measure.
b. Select one or two individual program(s) from the population with a probability based on fitness (with reselection allowed) to participate in the genetic operations in (c).
c. Create new individual program(s) for the population by applying the following genetic operations with specified probabilities:
i. Reproduction: Copy the selected individual program to the new population.
ii. Crossover: Create new offspring program(s) for the new population by recombining randomly chosen parts from two selected programs.
iii. Mutation: Create one new offspring program for the new population by randomly mutating a randomly chosen part of one selected program.
3. After the termination criterion is satisfied, the single best program in the population produced during the run (the best-so-far individual) is harvested and designated as the result of the run. If the run is successful, the result may be a solution (or approximate solution) to the problem.
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© 2006 Kumaravel & Project Team
1. Randomly create an initial population (generation 0) of individual computer programs composed of the available functions and terminals.
2. Iteratively perform the following sub-steps (called a generation) on the population until the termination criterion is satisfied:
a. Execute each program in the population and ascertain its fitness (explicitly or implicitly) using the problem’s fitness measure.
b. Select one or two individual program(s) from the population with a probability based on fitness (with reselection allowed) to participate in the genetic operations in (c).
c. Create new individual program(s) for the population by applying the following genetic operations with specified probabilities:
i. Reproduction: Copy the selected individual program to the new population.
ii. Crossover: Create new offspring program(s) for the new population by recombining randomly chosen parts from two selected programs.
iii. Mutation: Create one new offspring program for the new population by randomly mutating a randomly chosen part of one selected program.
3. After the termination criterion is satisfied, the single best program in the population produced during the run (the best-so-far individual) is harvested and designated as the result of the run. If the run is successful, the result may be a solution (or approximate solution) to the problem.
Related Posts:
- ABSTRACT
- INTRODUCTION
- JOB SHOP SCHEDULING
- Search Methods and Optimization Techniques
- Genetic Algorithm
- Mechanism of GA
- Evolutionary Steps of Genetic Programming -1
© 2006 Kumaravel & Project Team
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