-
Notifications
You must be signed in to change notification settings - Fork 20
/
floatGradExample.m
101 lines (77 loc) · 2.09 KB
/
floatGradExample.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
echo off
%load seed.mat
rand('seed',0);
echo on
% This script shows how to use the ga. You should see the demos for
% more information as well. gademo1, gademo2, gademo3
global bounds
% Crossover Operators
xFns = 'arithXover heuristicXover simpleXover';
xOpts = [2 0; 2 3; 2 0];
% Mutation Operators
mFns = 'boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation';
mOpts = [4 0 0;6 10 3;4 10 3;4 0 0];
% Termination Operators
termFns = 'maxGenTerm';
termOps = [10];
% Selection Function
selectFn = 'normGeomSelect';
selectOps = [0.06];
% Evaluation Function takes two options
% prob to use gradient, prob to perform Lamarkian evolution
evalFn = 'gaZBGradEval';
evalOps = [1.00 1.00];
% Bounds on the variables
bounds = [-3 12.1; 4.1 5.8];
% GA Options [epsilon float/binar display]
gaOpts=[1e-6 1 1];
% Generate an intialize population of size 80
startPop = initializega(80,bounds,evalFn,evalOps,[1e-6 1]);
evalOps = [1.00 0.00]; % 1 - Peform learning 0-Do not update
% Lets run the GA using Baldwinian Evolution
[x endPop bestPop trace]=ga(bounds,evalFn,evalOps,startPop,gaOpts,...
termFns,termOps,selectFn,selectOps,xFns,xOpts,mFns,mOpts);
% x is the best solution found
x
pause
% endPop is the ending population
endPop
pause
% bestPop is the best solution tracked over generations
bestPop
pause
% trace is a trace of the best value and average value of generations
trace
pause
% Plot the best over time
clf
plot(trace(:,1),trace(:,2));
pause
% Add the average to the graph
hold on
plot(trace(:,1),trace(:,3));
pause
% Lets run the GA using Lamarkian Evolution
evalOps = [1.00 1.00];
[x endPop bestPop trace]=ga(bounds,evalFn,evalOps,startPop,gaOpts,...
termFns,termOps,selectFn,selectOps,xFns,xOpts,mFns,mOpts);
% x is the best solution found
x
pause
% endPop is the ending population
endPop
pause
% bestPop is the best solution tracked over generations
bestPop
pause
% trace is a trace of the best value and average value of generations
trace
pause
% Plot the best over time
clf
plot(trace(:,1),trace(:,2));
pause
% Add the average to the graph
hold on
plot(trace(:,1),trace(:,3));
pause