genetic algorithm - aaditya pare

7/31/2019 Genetic Algorithm - Aaditya Pare

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Genetic Algorithm

Priya Mishra

Department of Computer Science,RKDF Institute of Science and

Technology,

RGPV University, Bhopal

[email protected],

Aaditya Pare

Department of Computer Science,RKDF Institute of Science and

Technology,

RGPV University, Bhopal

[email protected]

Abstract-This paper attempts to examine the way

as how Genetic Algorithm (GA) can be

employed in finding the best solution amongst

a given number of possible solutions, the basic

principle being extracted from the evolution

and adaptation of organisms. The thought of

Genetic Algorithm primarily originates from

the perception of Charles Darwin who thought

that combination of selection and variation is

what that makes evolution of organisms perfectly

adaptable to the environment. As generations

pass, better adaptable organisms are born and

the system slowly reaches to a most

favourable point. GA makes use of this principle

and eventually converges to the best optimal

solution amongst a set of given solutions. In

searching for a solution, a population of

candidate is generated, evaluated, selected,

and reproduced with modification to produce

the candidate population until no further

improvement can be made or after certain

numbers of generations have generations have

evolved, as according to the need of the problem.

Keyword: Evolution, Selection, Mutation,Crossover, Optimum, Adaptation, Fitness.

I. Introduction

Genetic Algorithms are probabilistic searchapproach, which are founded on the ideas of

evolutionary processes. The Genetic Algorithmprocedure is based on Darwinian principle of thesurvival of the fittest. Genetic Algorithm makesuse of the principles of selection and evolutionto produce several solutions to a given problem.one must note that the underlying principle ofGenetic Algorithm arises from the evolutiontheory only; it is the rigidity and flexibility of this

principle which has made Genetic Algorithm sopopular amongst optimization field and findingsolution to real world problems. As againsttraditional methods, Genetic Algorithm is suited formany real world problems which involve finding

optimal parameters. Not only does GeneticAlgorithm provide an alternative method to solving

problem, it consistently outperforms other

traditional methods in most of the problems link.They do an excellent job of searching through alarge and complex search space. GeneticAlgorithms are more effective in finding theoptimum, be it in a state-space search or insurfaces. Selection, Variation and Elimination arethe three bricks upon which the whole structure ofGenetic Algorithm is constructed.

II. Benefit from genetic algorithm?

Genetic Algorithms are proven to be anenormously powerful and successful problemsolving strategy, demonstrating the power ofevolutionary principles. Moreover, the solutionsthey come up with are often more competent,

more elegant, more complex as compared toother traditional problem solving techniques.Genetic Algorithm is beneficial to nearlyeveryone, once the correct mode ofrepresentation for the problem is chosen plusthe relative fitness of solutions is comparedcorrectly. Genetic Algorithm is useful and efficientwhen1. The search space is large, complex or poorly

understood.2. Traditional non-linear search methods fail.3. No mathematical analysis is available.4. Fitness landscape is non-linear and changes

over time.


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5. Multi-modal or n-dimensional search spaceexists.

III. The Principle

The Algorithm- Genetic Algorithm is, in itstrue sense, associated with sexual reproduction

in which the genes of two parents combineto form those of their children. When it isapplied to problem solving, the basic logicis that we can create an initial populationof individuals representing possiblesolutions to a problem we are trying tosolve. Each of these individuals has anassociated fitness measure (fitness function)that makes them more or less fit as members ofthe population. The most fit members willhave a higher probability of mating than theless fit member, to produce offspring thathave a significant chance of retaining the

desirable characteristics of their parents.Therefore the algorithm identifies theindividuals with the optimizing fitness values,and those with lower fitness will naturally getdiscarded from the population. Successivegenerations improve the fitness ofindividuals in the population until theoptimal solution is met. Hence, thealgorithm can be framed as:1. Encode the problem in a binary string or

an array of integers or decimal numbers.

2. Random generate the populationrepresenting a group of possible solutions.

3. Calculate the fitness value for eachindividual

IV. Methods Of RepresentationBefore employing Genetic Algorithms in findingsolution to a problem, the problem (possiblesolutions) needs to be encoded in a computerrecognizable form. There are many ways ofrepresenting the problem where each methodhas its own advantages and disadvantages, few ofthem being:

1. Encoding the problem in binary string i.e.sequences of 1s and 0s, where digit ateach position makes up some part of thesolution.

2. Encoding the problem in array of decimalnumbers or integer numbers where againeach digit represents some part of thesolution.

3. Encoding the problem in array of strings. Thismethod is used in grammatical encodingapproach employed to generate neuralnetworks.

4. Making use of genetic programming in

which problem is represented in form of datastructure known as tree.

V. Genetic Operators

Genetic operators are applied to improve theperformance of the population of behaviours.One cycle of testing all of the candidates isdefined as a generation, and is repeated until agood behaviour is then applied to the real world.The operators here are described with regard tothe evolution and adaptation procedure i.e.

parents are selected based on their fitness andthey reproduce to create offspring which are betteradapted to the next generation. The sameconcept is applied while dealing with extraction ofa better solution amongst the given possiblesolution in the real world.

Selection- The selection procedure randomly

selects individuals from current populationfor development of the next generation.Again, in the next generation the selectedcandidates are chosen for the next solutionor cycle. Various types of selection

procedures are available, from which anycan be chosen as best suited to

programmer, few of them being Roulette-wheel selection, Generational selection,Hierarchical selection, Rank selection.

Crossover- The crossover procedureinvolves combining two selected individualsabout a crossover point thereby creating

two new individuals which represent thenext generation. In case of asexualreproduction, a single individual is replicatedinto the new population. There are manydifferent kinds of crossover; the mostcommon type is single point crossover. Insingle point crossover, the children take onesection of the chromosome from each

parent, thus inheriting good behaviour fromboth each. Sometimes parents are copieddirectly to the new population when nocrossover occurs. The probability ofCrossover occurring is usually 60% to 70%.

1 0 1 1 0 0 1 0

1 1 1 0 0 1 0 1

1 0 1 1 0 1 0 1

1 0 1 1 0 0 1 0

1 0 1 1 1 0 1 0

Row 3 indicates a new hybrid with crossover(single point) taking place between 4 th and 5th

positions in the chromosomes of its parents. Row5 indicates mutation taking place at 5 th position.(Here the problem is coated in strings of binarynumbers 0s and 1s).

Mutation- After selection and crossover,theres a new population full of selectedcandidates representing next generation (a


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merely good solution).When no crossoveroccurs, individuals are copied directly andothers are produced by crossover. In order toensure that the individuals are not exactlythe same, a provision for mutation is made.The mutation procedure randomly modifies

the genes of an individual subject to asmall mutation factor, introducing furtherrandomness into the population. In otherwords, it periodically makes randomchanges in one or more members of thecurrent population, yielding a new candidatesolution. The probability of mutation isusually between 1 and 2 tenths of a

percent. Mutation is important in ensuringgenetic diversity within the population.

VI. Why Genetic Algorithm

Genetic Algorithms can identify and exploit

regularities in the environment, and converges onsolutions (can also be regarded as locating the localmaxima) that were globally optimal. This methodis very effective at finding optimal or nearoptimal solutions to a wide variety of problems,

because it does not impose limitations requiredby traditional methods such as gradient search,random search etc. The Genetic Algorithmtechnique have advantages over traditional non-linear solution techniques that cannot alwaysachieve an optimal solution.The method is very different from classicaloptimization algorithms-

1. It uses encoding of the parameters,not the parameters themselves.2. The search is more exhaustive in agiven amount of time.

3. Due to its probabilistic nature ratherthan deterministic, it yields different solutionson different runs.

4. Explores the solution space inmultiple directions rather than in singledirection.

VII. Applications

Genetic Algorithm can primarily be employedfor problem solving and for modelling. Itsplasticity and efficiency of solving problemsmakes it a more favourite choice among thetraditional methods, namely gradient search,random search and others. Genetic Algorithmhave been widely studied, experimented and can

be applied to many scientific and engineeringproblems, in business and entertainment.

1. Artificial life- Genetic Algorithm the mostwidely and prominently used computationalmodels of evolution in artificial-life

systems. Researches on Genetic Algorithmin given illustrative examples in which thegenetic algorithm is used to study how

learning and evolution interact, and tomodel ecosystems, immune system, cognitivesystems, and social systems.

2. Artificial Neural Network Applications-Genetic Algorithm has been increasinglyapplied in ANN design in several ways;

topology optimization, genetic trainingalgorithms and control parameteroptimization. In addition Genetic Algorithmhave been used in many other innovativeways, for instance, creating new indicators

based on existing ones, selecting goodindicators and in complementing fuzzylogic.

3. Information System Applications-Distributedcomputer network topologies are designed

by a Genetic Algorithm, using three differentobjective functions to optimize networkreliability parameters, namely diameter,

average distance, and computer networkreliability. The Genetic Algorithm hassuccessfully designed networks with 100orders of nodes.

4. Genetic Algorithm in Business and TheirSupportive Role in Decision Making-GeneticAlgorithms can be used to solve manydifferent types of business problems infunctional areas such as finance marketing,information systems, and

production/operations. Within these functionalareas, Genetic Algorithm can perform avariety of applications such as tactical assetallocation, job scheduling, machine-partgrouping, and computer network design.

5. Astronomy And Astrophysics-GeneticAlgorithm are useful in solving 3 types of

problems: Fitting and rotation curve of agalaxy based on observed rotationalvelocities of its components, determiningthe pulsation period of a variable star basedon time-series data, and solving for thecritical parameters in a magnetohydrodynamic model of the solar wind.

6. Machine And Robot Learning-Genetic

Algorithm can be used for many machine-learning applications, including classificationand prediction, protein structure prediction.Genetic Algorithm have also been used todesign neural networks, to evolve rules forlearning classifier systems or symbolic

production systems, and to design andcontrol robots.

7. Economic And Financial Markets- GeneticAlgorithm has been used to model

processes of innovation, the development ofbinding strategies, and the emergence ofeconomic markets. An investment firm

California that manages over $2.2 billion usesGenetic Algorithm to make investment


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decisions for all their financial services.Genetic Algorithm can also be used to predictexchange rates of foreign currencies up to onemonth ahead.

8. Mathematics And Algorithmic- GeneticAlgorithm can offer a great help in solving

pure mathematical problems such as high-order nonlinear partial differential equations.They can also be used in sorting networkwhich is a fixed list of comparisons performedon a set of a given size and eachcomparison; two elements are compared andswapped if not in order.

9. Optimization-Genetic Algorithm have beenused in a wide variety of optimization tasks,including numerical optimization, andcombinatorial optimization problems suchas travelling salesman problem(TSP), circuitdesign, job shop scheduling and video and

sound quality optimization. In addition tothese, Genetic Algorithms find theirapplication Aerospace engineering,Geophysics, Military and law enforcement,Data Mining, Cryptography, Immunesystem models, Ecological models, Machineand robot learning and can be employedwith more intensiveness in many otherfields.

VIII. Limitations

Although because of its simplicity andclassiness, Genetic Algorithm have proventhemselves as efficient problem solving strategy,yet they cannot be considered as universalremedy. Some limitations do persist with them

1. The method chosen for representing theproblem must be strong and firm, it mustwithstand random changes or otherwise wemay not obtain the desired solution.

2. Fitness function must be chosen carefully. Itshould be able to evaluate correct fitness levelof each candidate. If the fitness function ischosen poorly or defined vaguely, the GeneticAlgorithm may be unable to find a solution to

the problem, or may end up solving the wrongproblem.

3. Genetic Algorithms do not work well when thepopulation size is small and the rate of changeis too high.

4. Another drawback of Genetic Algorithm isthat solution is better only in comparisonto other, presently known solutions; itcannot make out the optimum solution ofits own.

5. Sometimes if an individual more fit thanits associated competitors arrives before itshould have, it abruptly decreases the size of

population, leading the algorithm toconverge on the local optimum without

examining the rest of the search space.This problem is popularly known asPremature Convergence.

IX. Conclusion And Future Work

The power of evolution has surely refined each and

every step it has undergone in its way, and onecannot reject its usefulness in anyway becausewithout it none of the immeasurable advanceswill be indebt to genetic algorithms would have

been possible, and of course, the main drivingforce being Charles Darwins simple, powerfulintellectual: that the random chance of variation,together with law of selection, is a problemsolving technique of massive and limitlessapplication. The algorithm is one of the best

problems solving tool in the present scientificand commercial world. Though its theoretical

journey, as research continued to be productive,

genetic algorithms soon jumped into thecommercial sector. Today, Genetic Algorithmare related to solving problems of everydayinterest in many diverse fields. Due to itsintrinsic parallelism, the comprehensiveness withwhich this algorithm is applied in so manyareas is no less than astounding. However,several improvements can be made in order thatGenetic Algorithm could be more generallyapplicable. Future work will continue in

process of building robotic system throughevolution and many more specific tasks and asresearch is on going, we would surely witness

some of the most flawless advancements in GeneticAlgorithm application fields.

References

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