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ISSN: 0975-766X
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Research Article
EVOLUTIONARY ALGORITHM – A REVIEW
Nithya.S, Dhanuja.B, M.AshaJerlin
VIT University, Vellore.
Email: [email protected]
Received on 25-10-2016
Accepted on 02-11-2016
Abstract:
Bio inspired computing, is a field which provides various principles encountered in nature for solving/tackling
complex problems by using the computational model of the principle. Numerous researches have been done in this
area. Bio inspired computing is where the studies have been done in biological field to identify new technique which
can be effectively applied in many computer science related problems with mathematical computation. Bio inspired
computing is closely related to the field of artificial intelligence. There are many algorithms/principles which
effectively provide a new optimization technique.
Bio inspired computing is an emerging technique in this era. In which more and more algorithms and techniques are
yet to be identified to provide optimal solution to the complex problems. Bio inspired algorithms are experimental
and this can be applied when the there is a less knowledge about the problem. These algorithms are inspired by
processes observed in nature by the living species.
This paper will provide a description about the various evolutionary and emergent algorithms their basic flow of
algorithm, applications, advantages and disadvantages.
Keywords: Evolutionary algorithm,bio inspired algorithm, emergent algorithm,ant colony optimization,cuckoo
search.
Introduction:
Biologically motivated computing or Bio inspired computing aims in providing solution to the problem in various
fields like classification and decision making,pattern recognition,machine learning,computer security,image
processing,data mining,etc., by observing the behaviours of the living organisms like animals,birds,insects,etc.,.It
also motivates to design and implement new and improved technique from the behaviours of biological organisms.In
addition to other applications, bioinspired computing has a major implication in scientific research.
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Related Work:
There are different types of algorithms identified in Bio inspired computing and some of them are Evolutionary
algorithms and emergent algorithms.
The emergent algorithm exhibit a emergent behaviour.Some of the examples of emergent algorithm are ant colony
optimization,cuckoo search, etc.,.
A. Evolutionary Algorithm
The EA algorithm provides better solution to the complex problem by creating people data structures and performing
the survuival of the fittest function.This algorithm can be applied to any complex problem without any prior
knowledge or special tuning.Since it contains various methods it is easy to select a suitable method for the given
problem.The most important evolutionary algorithms are evolution strategies,genetic algorithms,genetic
programming[1].A large number of features areshared common in this algorithm.They focus on finding the fittest
among the population based approach.It also includes some natural process like selection,recombination,mutation and
evaluation.
The flow of evolutionary algorithm is
 Set initial population to 0.
 EVALUATION: Set the fitness criteria for selecting the parents.
 SELECTION: selecting the parents that match the criteria.
 RECOMBINATION: combine the pairs of parents.
 MUTATION: perform mutation of offsprings.
 REPLACEMENT: perform replacement of the selected some parents with it’s new offsprings.This process
repeats until it produces a best solution.
a. Genetic Algorithm:
Genetic algorithm was proposed by Holland in 1975.Genetic Algorithm has been an obivous mapping of natural
evolutionary processes into a computer system.Under Evolutionary algorithm,Genetic algorithm is one among the
most successful algorithms.The charles darwin theory,survival of the fittest has been followed effectively in this
algorithm.This solution derived from this algorithm is being stored in the form of coded bits and the process is known
as Binary standard coding.A large number of candidate solution for the problem has been maintained by this
algorithm and a set of stochastic operators is applied iterativly.The individuals in the group are evaluated based on
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fitness function.The fitness evaluation comes under two major criteria.They are classification accuracy and
sizeofsubset.The individuals are allowed to (stochasticoperators)reproduce(Selection),crossover and mutate.The
advantages of using genetic algorithm is:

It provides an efficient method for optimization.

It improves the solution on each iteration.

It is faster and requires lower memory space.

The traditional search method fails.

It is useful when the search space is poorly known or it is highly complex.
The GA can be used in a situation where the problem is already solved by using GA/when the existing problem has to
be hybridized and also when there is a need if an exploratory tool to examine new approaches.
b. Genetic Programming:
The genetic programming is an systematic,domain independent technique in which the computer automatically solves
the problem without any explicit instruction.It includes variable length representation.The individuals in genetic
programming population is in the form of computer programs[7].During the transformation to each generation, it
transforms the population of programs into another population of programs.The genetic programming process
involves in
i.
Creating intial population of programs randomly
ii.
Each program is executed and they are evaluated against the fitness criteria.
iii.
Selecting the programs that match the probablity of fitenss criteria.
iv.
The individual programs are created by applying the genetic operations.
v.
This process is repeated until the best solution is obtained.
The advantages of using genetic programming is that it can be used to design computer algorithms , to schedule task
and to solve optimization problems. They can be applied to the complicated systems to find a better solution in an
easiest way.
c.
Evolution Strategies:
Advancement methodologies get motivation from standards of organic development. We expect a populace, P, of
supposed people. Every person comprises of an answer or protest parameter vector xR n (the obvious attributes)
and further endogenous parameter s (the concealed qualities), and related wellness esteem, f(x). Now and again the
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populace contains one and only person. People are likewise signified as guardians or posterity, contingent upon the
connection. In a generational method,
1. One or a few guardians are picked from the populace (mating choice) and new off- spring are created by
duplication and recombination of these guardians.
2. The new posterity experience change and get to be new individuals from the populace.
3. Natural choice diminishes the populace to its unique size.
Some basic Selection and sampling plans in Evolutionary strategies are as per the following:
(1+1)-ES: This is a basic choice selection mechanism. This mechanism involves in selecting the mutant as the parent
of the next generation only if the result of the parent mutant’s fitness should be at least as good as its parent fitness.
(1+)-ES: In general, it involves in creating  mutants from the parents and these mutants then compete with their
parents.
(1,)-ES: The parent will be disregarded if its mutant is selected as a parent of nest generation.
d. Evolutionary Programming:
Evolutionary programming has been developed by L.J.Fogel in 1962 in USA.EP is applied to machine learning task
by finite state machines and numerical optimization.EP includes only mutation process and the recombination is not
used in this method.The representation of EP in the form of real-valued vectors.In parent selection,each individual
creates one child by mutation.
basic evolutionary programming algorithm:
Create an initial population and initialize it.
Evaluate the fitess of each individual
For each individual create a offspring(MUTATION)
Evaluate it’s fitness.
Select new population and continue the process.
B. Swarm Intelligence:
The swarm intelligence is also known as collective intelligence,as it makes use of collective behaviour of
homogenous species to perform a task.It is a decentralized,self organized algorithm and swarm actually denotes
single group as an individual because the individuals in the group are relatively homogenous. They can perform the
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actions without any centralized control and they are self-organized in nature.They form a loosly structured collection
of interacting agents and these agents can contribute to and benefit from the group[8].
The examples of swarm intelligence in nature are colonies of ants,flocks of birds,herding of animals,bacterial
growth and schooling fish.The advantages of using swarm intelligence technique are:
Scaliblity: The increase in the size ,increases the performance without any need to redefine how its parts should
interact.
Parallel:The individuals in the group can perform different actions at the same time in different places,which
increases the flexiblity.
The individuals in the group can be added or removed without any influence in their structure.This system can get
easily adopted to the new environment.
Robust: The failure of single individual in the group has the little impact on the performance.
Applications of SI:
The SI techniques are being applied in/as

U.S Military for managing unmanned vehicles.

For planetary mapping in NASA.

Load balancing in telecommunication systems.
a) Practical Swarm Optimization:
PSO is a stochastic optimization technique based on population.In computational search space, PSO searches for an
optimal solution.The PSO individuals has an ablility to improve themselves and often they achieve this by imitating
the behaviour of their neighbours.The individuals of the group learn from the previous experiences and their
experiences around them. Hence,the each individual in the population gradually move towards the “better solution”
by which the whole group gradually move towards the better solution space.
b) Ant colony Optimization:
The concept is inspired by ant behaviors. Ants find briefest way to sustenance source from home. Ants store
pheromone along voyaged way which is utilized by different ants to take after the trail. This sort of aberrant
correspondence by means of the neighborhoodenvironment is called stigmergy [1].
Behavior of ants:
 2 ants begin with equivalent likelihood of going on either way.
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 The insect on shorter way has a shorter back and forth time from its home to the sustenance.
 The thickness of pheromone on the shorter way is higher on account of 2 goes by the insect.
 The next ant takes the shorter route.
Over numerous emphases, more ants start utilizing the way with higher pheromone, subsequently advance fortifying
it.After some time, the shorter path is almost exclusively used.
Advantages of ACO:
 Exhibits Inherent parallelism.
 This algorithm frequently provides good solution.
 It can also be used in travelling salesman problem to solve the problem of finding minimal distance between the
cities that visits each city only once.
 It can also be used when there is a need of dynamic update (i.e., choosing the new distance or the shortest)
Disadvantages of ACO:
 Theoretical analysis is not an easy task
 Sequences of random decisions (as they are completely dependent on each iterations)
 Probability distribution changes in each emphases
 It involves more theoretical research than experimental.
 Time it takes to converge is uncertain.
c) Cuckoo search
Cuckoo search is one of the optimization algorithm proposed by Xin-She Yang[8] and Suash Deb in 2009.It
inspiresobligate brood parasite mechanism.Some species lay eggs in another host species nest.It is mainly to find an
global optimum of a function.
Cuckoo behaviour:
 Cuckoos have an aggressive reproduction strategy in which they lay eggs in another host nest.
 Some cuckoo species has an ability to imitate the color and patterns of the host species. So that it reduces the
probability of abandoned and thereby increases the productivity.
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 If the surrogate species identifies that the egg is it’s own, then it will simply throw the egg or it will abandon the
nest and bulit a new nest somewhere.
 Cuckoo places it’s eggs in the nest in which the eggs where newly laid because the cuckoo’s eggs will hatch
slight earlier than others. when it’s first hatched then it will throw all the host species eggs so that the food
sharing to the cuckoo chick gets increased.
The main aim of Cuckoo search is to replace the existing solution(eggs in the nest) with the potentially new
solution(cuckoo’s egg).
Three rules for cuckoo search

Each cuckoo lays only one egg at a time and it dumps the egg in the randomly chosen nest.

The nests that contains high quality of eggs will get carried over to the next generation.

The probability of finding an alien egg is P[0,1].
d) Firefly algorithm
Firefly algorithm has been proposed by Xin-She Yang[8].The Fireflies exhibit flashing behavior.It produces bright
cold light with little elevation of temperature.All Fireflies are unisex.Being able to glow has several benefits, they
are:

Helps the fireflies in mate selection.

To attract its potential prey.

As a warning mechanism.
Different type of firefly glow in different ways.So,each fireflies looks for the glow of the mate it needs regardless of
their sex[5][3].When the distance increases,attraction towards light decreases with brightness[4].
basic flow of firefly algorithm:
i.
Generate initial population.
ii.
Evaluate fitness of all fireflies.
iii.
Update the fitness value(Light intensity value) of fireflies.
iv.
Rank the fireflies and update their position based upon their fitness value.
v.
Perform iteration until the optimal result is obtained.
This algorithm can efficiently deal with the non-linear,multi-model optimization problems.It can be useful in many
applications like solving travelling salesman problem,scheduling,structural design,dynamic problems etc.
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Conclusion:
Bio-inspired algorithm is the most upcoming and new algorithm which has been widely used in computer science.
This paper provides an overview of an algorithm and their catagories.
The nature-inspired algorithms has been incoperated as an optimization technique in various fields likedata
mining,bio informatics,robotics,gametheory,image processing,distributed computing and so on.
This also produces most significant and outstanding results.There is a huge scope and opportunites to explore more
algorithms.
Reference:
1.
Binitha S and S Siva Sathya.A survey of bio inspired optimization algorithms.International journal of soft
computing and engineering,ISSN:2231-2307.volume-2.Issue-2.May 2012.
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Sankalap Arora and Satvir Singh.The firefly optimization Algorithm: Convergence Analysis and Parameter
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