by Antonio Carvajal-Rodríguez ().

Área de Genética, Departamento de Bioquímica, Genética e Inmunología. Universidad de Vigo, Spain.

**A. Carvajal-Rodríguez 2004. GASP: Genetic Algorithm (based on) Surviving
Probability. Online Journal of
Bioinformatics 5: 23-31.**

**SGA Algorithm**

1.- Generate randomly N individuals.

2.- Calculate the fitness of each individual in the population.

3.- Select randomly a pair of parents from current population, the probability of selection being an increasing function of fitness (fitness proportionate selection).

4.- Obtain a pair of sons by recombination from each pair of parents.

5.- Repeat step 3 until the population size N is reached.

6.- The new population substitutes the old one.

7.- Mutate and iterate using this new population.

**GASP Algorithm**

1.- Generate randomly Nini individuals

2.- Select randomly a pair of parents

3.- Obtain one son by recombination from each pair of parents . Add one more attempt to the particular counter of one progenitor.

4.- Evaluate the fitness of each son. The fitness is obtained dividing the objective function value by

Kwhich is the maximum or a reasonable supreme (higher than the maximum) of the objective function. A son will survive when its fitness value is higher than a number randomly generated from an uniform between 0 and 1.5.- Repeat step 2 until the maximum population size is reached or all parents have spent their fixed number (R) of attempts.

6.- The new population substitutes the old one.

7.- Mutate and iterate using this new population.

**GASP Executable**

** Linux (upon request)**

** DOS (upon request)**

**Code (in C) (upon request)**

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