Project

Genetic Algorithim for maximal function

VVeeresh H B11 Apr 2018

The algorithm begins by creating a random initial population.
The algorithm then creates a sequence of new populations. At each step, the algorithm uses the individuals in the current generation to create the next population. To create the new population, the algorithm performs the following steps:
1. Scores each member of the current population by computing its fitness value. These values are called the raw fitness scores.
2. Scales the raw fitness scores to convert them into a more usable range of values. These scaled values are called expectation values.
3. Selects members, called parents, based on their expectation.
4. Some of the individuals in the current population that have lower fitness are chosen as elite. These elite individuals are passed to the next population.
5. Produces children from the parents. Children are produced either by making random changes to a single parent—mutation—or by combining the vector entries of a pair of parents—crossover.
6. Replaces the current population with the children to form the next generation.
The algorithm stops when one of the stopping criteria is met.

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clear all
close all
clc

%defining our search space

x = linspace(0,0.6,100);
y = linspace(0,0.6,100);

%creating a 2-d mesh

[xx yy] = meshgrid(x,y);

%evaluating the stalagamite func

for i = 1:length(xx)
    for j = 1:length(yy)
        input_vector(1) = xx(i,j);
        input_vector(2) = yy(i,j);
        F(i,j) = LinearEq(input_vector);
    end
end

surfc(xx,yy,-F)
shading interp

[x,fval] = ga(@LinearEq,2)

Genetic Algorithim for maximal function

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Ship work like thisin your own course.

Talk to a career counsellor

Ship work like this
in your own course.