Mutation Algorithms for Real-Valued Parameters (GA)
Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. In each generation chromosomes(our solution candidates) undergo mutation and crossover and selection to produce a better population whose chromosomes are nearer to our desired solution. Mutation Operator is a unary operator and it needs only one parent to work on. It does so by selecting a few genes from our selected chromosome (parent) and then by applying the desired mutation operator on them.
In this article, I will be talking about four Mutation Algorithms for real-valued parameters –
1) Uniform Mutation
3) Boundary Mutation
4) Gaussian Mutation
Here ,we are considering a chromosome with n real numbers (which are our genes) and xi represents a gene and i belongs to [1,n].
Uniform Mutation –
In uniform mutation we select a random gene from our chromosome, let’s say xi and assign a uniform random value to it.
Let xi be within the range [ai,bi] then we assign U(ai,bi) to xi
U(ai,bi) denotes a uniform random number from within the range [ai,bi].
Algorithm – 1. Select a random integer number i from [1,n] 2. Set xi to U(ai,bi).
Boundary Mutation –
In boundary mutation we select a random gene from our chromosome , let’s say xi and assign the upper bound or the lower bound of xi to it.
Let xi be within the range [ai,bi] then we assign either ai or bi to xi.
We also select a variable r= U(0,1) ( r is a number between 0 and 1).
If r is greater than or equal to 0.5 , assign bi to xi else assign ai to xi.
Algorithm – 1. select a random integer number i form [1,n] 2. select a random real value r from (0,1). 3. If(r >= 0.5) Set xi to bi else Set xi to ai
Non-Uniform Mutation –
In non-uniform mutation we select a random gene from our chromosome, let’s say xi and assign a non-uniform random value to it.
Let xi be within the range [ai,bi] then we assign a non-uniform random value to it.
We use a function,
where r2 = a uniform random number between (0,1)
G = the current generation number
Gmax = the maximum number of generations
b = a shape parameter
Here we select a uniform random number r1 between (0,1).
If r greater than or equal to 0.5 we assign (bi-xi) * f(G) to xi else we assign (ai+ xi) * f(G).
Algorithm – 1. Select a random integer i within [1,n] 2. select two random real values r1 ,r2 from (0,1). 3. If(r1 >= 0.5) Set xi to (bi-xi) * f(G) else Set xi to (ai+ xi) * f(G)
Gaussian Mutation –
Gaussian Mutation makes use of the Gauss error function . It is far more efficient in converging than the previously mentioned algorithms. We select a random gene let’s say xi which belongs to the range [ai,bi]. Let the mutated off spring be x’i. Every variable has a mutation strength operator (σi). We use σ= σi/(bi-ai) as a fixed non-dimensionalized parameter for all n variables;
Thus the offspring x’i is given by —
x’i= xi + √2 * σ * (bi-ai)erf-1(u’i)
Here erf() denotes the Gaussian error function.
erf(y)=2⁄√π ∫y0 e-t2 dt
For calculation ui’ we first select a random value ui from within the range (0,1) and then use the following formula
if(ui>=0.5) u’i=2*uL*(1-2*ui) else u’i=2*uR*(2*ui-1)
Again uL and uR are given by the formula
uL=0.5(erf( (ai-xi)⁄(√2(bi-ai)σ) )+1) uR=0.5(erf( (bi-xi)⁄(√2(bi-ai)σ) )+1)
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