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>Overview Of Genetic Algorithms , Theory And Applications

>Introduction

Genetic algorithms (GAs) are a powerful tool for global search and optimisation tasks of high complexity in science, technology, industry medicine, safety, economy, communication etc. They are a particular class of evolutionary algorithms, based on the mechanics of natural selection (biological evolution). It was developed by John Holland The idea behind GA’s is Darwinian evolution The most basic concept is the strong tend to adapt and survive while the weak tend to die out. That is, optimization is based on evolution, “Genetic Inheritance” and the “Survival of the fittest” concept. These algorithms employ classical operations (selection, crossover, mutation).

Genetic algorithms (GAs) can be easily modified in order to account for specific features of the given computational problem. Particularly well suited for hard problems where little is known about the underlying search space

Genetic algorithms are best for searching for new solutions and making use of solutions that have worked well in the past. It works on large population of candidate solutions that are repeatedly subjected to selection pressure (survival of the fittest) and which undergo naturally occurring genetic operators in search of improved solutions.

GENETIC ALGORITHMS

We use Genetic Algorithms in following situations

* When an acceptable solution representation is available
* When a good fitness function is available
* When it is feasible to evaluate each potential solution
* When a near-optimal, but not optimal solution is acceptable.
* When the state-space is too large for other methods

Genetic Algorithms vs Traditional Algorithm

Genetic algorithms are different in some fundamental ways from the traditional search techniques in the following ways.

1. GA’s work with a coding of parameter set, not the parameter themselves.
2. GA’s search from a population of points, not a single point.
3. Application of GA operators causes information from the previous generation to be carried over to the next.
4. GA’s use probabilistic rules, not deterministic rules.

BASIC COMPONENTS OF GENETIC ALGORITHMS

GAs are blind without the fitness function.The Fitness Function Drives the Population Toward Better Solutions and is the most important part of the algorithm. Most Important Parameters in GAs:

* Population Size

The algorithm is started with a set of solutions (represented by chromosomes) called population.

* Fittness Function

Determine the fitness of each member of the population

* Crossover Method

Crossover selects genes from parent chromosomes and creates a new offspring

* Mutation

This is to prevent fall all solutions in population into a local optimum of solved problem

* Reproduction (SELECTION)

Determine which strings are “copied” or “selected” for the mating pool and how many times a string will be “selected” for the mating pool .

GENETIC PROGRAMMING

Manipulate strings of instructions rather than strings of data.
Goal: Allow computers to develop their own software
(Survival of the fittest computer programs)

Genetic Programming starts with an initial population of randomly generated computer programs composed of functions and terminals appropriate to the problem domain. The functions may be standard arithmetic operations, standard programming operations, standard mathematical functions, logical functions, or domain-specific functions.

ADVANTAGES OF GENETIC ALGORITHMS

The main advantages of genetic algorithms are,

* A robust search technique
* GAs will produce “close” to optimal results in a
* “reasonable” amount of time
* Suitable for parallel processing
* Some problems are deceptive
* Can use a noisy fitness function
* Fairly simple to develop
* Makes no assumptions about the problem space
*

APPLICATIONS OF GENETIC ALGORITHMS

The possible applications of genetic algorithms are immense. Any problem that has a large search domain could be suitable tackled by GAs. A popular growing field is genetic programming (GP). Genetic Programming particularly used in machine learning, scientific modeling, and artificial life.

* Scheduling:

Facility, Production, Job, and Transportation Scheduling.

* Design:

Circuit board layout, Communication Network design,keyboard layout, Parametric design in aircraft.

* Machine Learning:

Designing Neural Networks, Classifier Systems, Learning rules.

* Robotics:

Trajectory Planning, Path planning.

* Combinatorial Optimization:

TSP, Bin Packing, Set Covering, Graph Bisection, Routing.

* Image Processing:

Pattern recognition.

* Business:

Economic Forecasting; Evaluating credit risks.
Detecting stolen credit cards before customer reports it is stolen.

* Medical:

Studying health risks for a population exposed to toxins.

CONCLUSION

Genetic algorithms (GAs) are a powerful tool for global search and optimisation tasks of high complexity in science, technology, industry medicine, safety, economy, communication etc. They are a particular class of evolutionary algorithms, based on the mechanics of natural selection (biological evolution).

Genetic algorithms (GAs) can be easily modified in order to account for specific features of the given computational problem. Particularly well suited for hard problems where little is known about the underlying search space.

Genetic algorithms are best for searching for new solutions and making use of solutions that have worked well in the past. It works on large population of candidate solutions that are repeatedly subjected to selection pressure (survival of the fittest) and which undergo naturally occurring genetic operators in search of improved solutions.

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