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Genetic Algorithms

Genetic algorithms (GAs): basic form

A generic GA (also known as an evolutionary algorithm [EA]) assumes a discrete search space $H$ and a function \[f:H\to\R,\] where $H$ is a subset of the Euclidean space $\R$.

The general problem is to find \[\arg\underset{X\in H}{\mathop{\min }}\,f\] where $X$ is a vector of the decision variables and $f$ is the objective function.

With GAs it is customary to distinguish genotype–the encoded representation of the variables–from phenotype–the set of variables themselves. The vector $X$ is represented by a string (or chromosome) $s$ of length $l$ made up of symbols drawn from an alphabet $A$ using the mapping \[c:A^l\to H\] The mapping $c$ is not necessarily surjective. The range of $c$ determine the subset of $A^l$ available for exploration by a GA. The range of $c$, $\Xi$ \[\Xi\subseteq {{A}^{l}}\] is needed to account for the fact that some strings in the image $A^l$ under $c$ may represent invalid solutions to the original problem.

The string length $l$ depends on the dimensions of both $H$ and $A^l$, with the elements of the string corresponding to genes and the values to alleles. This statement of genes and alleles is often referred to as genotype-phenotype mapping.

Given the statements above, the optimization becomes: \[\arg\underset{S\in L}{\mathop{\min g}}\,\], given the function \[g(s)=f(c(s))\]. Finally, with GAs it is helpful if $c$ is a bijection. The important property of bijections as they apply to GAs is that bijections have an inverse, i.e., there is a unique vector $x$ for every string and a unique string for each $x$.

Genetic algorithms and their Operators

Let $H$ be a nonempty set (the individual or search space) \[{{\left\{{{u}^{i}} \right\}}_{i\ in \mathbb{N}}}\] a sequence in \[{{\mathbb{Z}}^{+}}\] (the parent populations). \[{{\left\{ {{u}^{'(i)}} \right\}}_{i\in \mathbb{N}}}\] a sequence in \[{{\mathbb{Z}}^{+}}\] (the offspring population sizes), \[\phi:H\to \mathbb{R}\] a fitness function, \[\iota :\cup_{i=1}^{\infty}{{({{H}^{u}})}^{(i)}}\to\{\text{true},\text{false}\}\] (the termination criteria), \[\chi\in\{\text{true},\text{false}\},\] $r$ a sequence \[\left\{{{r}^{(i)}} \right\}\]

Define the collection $\mu$ (the number of individuals) via $H_\mu$. The population transforms are denoted by \[T:{{H}^{\mu }}\to {{H}^{\mu }}\] where \[\mu \in \mathbb{N}\ of recombination operators τ(i) : \[X_{r}^{(i)}\ to T(\Omega _{r}^{(i)},T\left( {{H}^{{{u}^{(i)}}}},{{H}^{u{{'}^{(i)}}}} \right)),\] $m$ a sequence of {m(i)} of mutation operators in mi, \[X_{m}^{(i)}\to T(\Omega _{m}^{(i)},T\left({{H}^{{{u}^{(i)}}}},{{H}^{u{{'}^{(i)}}}} \right)),\] $s$ a sequence of {si} selection operators s(i): \[X_{s}^{(i)}\timesT(H,\mathbb{R})\to T(\Omega _{s}^{(i)},T(({{H}^{u{{'}^{(i)+\chi {{\mu}^{(i)}}}}}}),{{H}^{{{\mu }^{(i+1)}}}})),\] \[\Theta _{r}^{(i)}\ in X_{r}^{(i)}\] (the recombination parameters), \[\Theta _{m}^{(i)}\\in X_{m}^{(i)}\] (the mutation parameters), and \[\Theta _{s}^{(i)}\ in X_{s}^{(i)}\] (the selection parameters).

To account for the cases where populations whose size not equal to their predecessors’, the following expression \[T:{{H}^{\mu}}\to {{H}^{{{\mu }'}}}\] accommodates populations that contain the same or different individuals. This mapping has the ability to represent all population sizes, genetic operators, and parameters as sequences.

The execution of a GA typically begins by random sampling with replacement from $A^l$. The resulting collection is the initial population, denoted by $P(0)$. In general, a population is a collection \[P=({{a}_{1}},{{a}_{2}},...,{{a}_{\mu }})\] of individuals, where \[{{a}_{i}}\in {{A}^{l}},\] and populations are treated as $n$-tuples of individuals. The number of individuals ($\mu$) is defined as the population size.

Following upon the work of Lamont and Merkle (Lamont, 1997) and others, the termination criteria and the other genetic operators(GOs)will be defined in more detail.

Since $H$ is a nonempty set, \[c:{{A}^{l}}\to H,\] and \[f:H\to \mathbb{R},\] the fitness scaling function can be defined as \[{{T}_{s}}:\mathbb{R}\to \mathbb{R}\] and a related fitness function as \[\Phi \triangleq {{T}_{s}}\circ f\circ c.\] In this definition it is understood that the objective function $f$ is determined by the application, while the specification of the decoding function $c[1]$ and the fitness scaling function $T_s$ are design issues.

After initialization, the execution proceeds iteratively. Each iteration consists of an application of one or more GOs. The combined effect of the GOs applied in a particular generation $t\in N$ is a transformation of the current population Italic textP(t) into a new population Italic textP(t+1).

In the population transformation $\mu ,{\mu}'\ in {{\mathbb{Z}}^{+}}$(the parent and offspring population sizes, respectively). A mapping $T:{{H}^{\mu }}\ to {{H}^{{{\mu }'}}}$ is called a population transformation (Italic textPT). If $T(P)={P}'$, then Italic textP is the parent population and ^{Superscript text}Italic textP/ the offspring population. If $\mu ={\mu }'$,then it is called the population size.

The Italic textPT resulting from a GO often depends on the outcome of a random experiment. This result is referred to as a random population transformation (Italic textRPT or random PT). To define Italic textRPT, let $\mu \in {{\mathbb{Z}}^{+}}$and $\Omega $ be a set (the sample space). A random function $R:\Omega \to T({{H}{\mu }},\bigcup\limits_{{\mu }'\ in {{\mathbb{Z}}^{+}}}^{{}}{{{H}^{{{\mu }'}}}})$ is called a Italic textRPT. The distribution of Italic textPTs resulting from the application of a GO depends on the operator parameters; in other words, a GO maps its parameters to a Italic textRPT.

Now that both the fitness function and Italic textRPT have been defined, let$\mu \in {{\mathbb{Z}}^{+}}$, Italic textX be a set (the parameter space) and $\Omega $ a set. The mapping $\Zeta :X\to T\left( \Omega ,T\left[ {{H}^{\mu }},\bigcup\limits_{{\mu }'\in {{\mathbb{Z}}^{+}}}^{{}}{{{H}^{{{\mu}'}}}} \right] \right)$ is a GO. The set of GOs is denoted as $GAOP\left( H,\mu ,X,\Omega \right)$.

There are three common GOs: recombination,mutation, and selection. These three operators are roughly analogous to their similarly named counterparts in genetics. The application of them in GAs is strictly Darwin-like in nature, i.e., “survival of the fittest.”

For the recombination operator let $r\in GAOP\left( H,\mu ,X,\Omega \right)$. If there exists $P\in {{H}^{\mu }},\Theta \in X$, and $\omega \in \Omega $, such that one individual in the offspring population ${{r}_{\Theta }}\left( P \right)$ depends on more than one individual of P,then r is referred to as a recombination operator.

For the mutation operator let $m\in GAOP\left( H,\mu ,X,\Omega\right)$. If for every $P\in {{H}^{\mu }}$, for every $\Theta \in X$, for every $\omega\in \Omega $, and if each individual in the offspring population ${{m}_{\Theta}}\left( P \right)$ depends on at most one individual of P, then m is called a mutation operator.

Finally, for the selection operator let $s\in EVOP\left( H,\mu ,X\times T\left(H,\mathbb{R}),\Omega \right) \right)$. If $P\in {{H}^{\mu }}$,$\Theta \in X$,$\Phi :H\to\mathbb{R}$in all cases, and satisfies $a\in {{s}_{\left( \Theta ,\Phi \right)}}(P)\Rightarrow a\in P$, then s is a selection operator.

Bold textEndnotes

[1] If the domain of c is total, i.e., the domain of c is all of A I, c is called a decoding function. The mapping of c is not necessarily surjective. The range of c determines the subset of Al available for exploration by the evolutionary algorithm.