genetic algorithms for optimization

GENETIC ALGORITHM FOR OPTIMIZATION

FETHİ CANDAN ANIL ERDİNÇ TÜFEKÇİ

İSMAİL HANCI

NUMERICAL METHODS IN OPTIMIZATION

OUTLINE

▸ What are the Genetic Algorithms (GA)

▸ Characteristics of GA

▸ Darwin’s Principle of Natural Selection

▸ Working of GA

▸ Components of GA

▸ Uniqueness of GA

▸ Procedure of GA

▸ Example

WHAT ARE THE GENETIC ALGORITHMS

Genetic Algorithms are search and optimization techniques based on Darwin’s Principle of Natural Selection.


HISTORY OF GA

▸ 1950 : Alan Turing proposed a “Learning Machine”

▸ 1957 : Alex Fraser published simulation of artificial selection of organisms

▸ 1960 : Hans-Joachim Bremermann published a series of papers in the 1960s that also adopted a population of solution to optimization problems, undergoing recombination, mutation, and selection. Bremermann's research also included the elements of modern genetic algorithms.

▸ 1975 : J.H.Holland, Adaptive in Natural and Artificial Systems.


https://en.wikipedia.org/wiki/Hans-Joachim_Bremermann

CHARACTERISTICS OF GA

▸ Stochastic in nature and less likely to get caught in local minima, so mostly used for global optimization problems

▸ Applies to both continuous and discrete optimization problems

▸ Parallel-Search procedure that can be implemented on parallel processing machines for speeding operations

▸ Heuristic method based on ‘Survival of the fittest’

▸ Useful when search space very large or too complex for analytic treatment


DARWIN’S PRINCIPLE OF NATURAL SELECTION

The basic principles formulated by Darwin:

1.The strongest survive and tips die (Natural Selection)

2.The new individual is obtained by crossing and there is mutation


WORKING GA

▸ GA encodes each point in a parameter space into a binary bit called chromosome

▸ Each point is associated with a fitness function

▸ Gene pool is a population of all such points

▸ In each generation GA constructs a new population using genetic operators

Crossover

Mutation


COMPONENTS OF GA

Encoding Schemes

Crossover Operators

Mutation Operators


STOCHASTIC OPERATORS

▸ Selection : Replicates the most successful solutions found in a population at a rate proportional to their relative quality.

▸ Recombination : Decomposes two distinct solution and then randomly mixes their parts to form new solutions.

▸ Mutation : Randomly pertubs a candidate solution.


UNIQUENESS OF GA

▸ Works with a coding of the parameter set, not the parameter themselves

▸ Search for a population of point and not single point

▸ Use objective function information and not derivatives or other auxiliary knowledge


PROCEDURE OF GA

FlowChart

SELECT THE BEST, DISCARD THE RESTSTART

GENERATE INITIAL POPULATION

CALCULATE THE COST FUNCTION

ALGORITHM END?

SELECTION

RECOMBINATION

MUTATION

NEXT GENERATION RESULT

END

N

N-n

n

N-n

N

YES


EXAMPLE

• A simple GA application

• Problem : Reach a target value with simple mathematical calculations.

• We have a target value such as 5, 37, 72.8, 231.38, etc.

• We have numbers { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }.

• We have operators { +, -, *, / }

• Totally we have 14 (10 + 4) different elements.

• We call each of one as GENE.

• A combination of GENEs, becomes a CHROMOSOME.


▸ We have to encode all of these different genes

▸ As a general attitude, we use binary bits

▸ Binary bits string

▸ The number of different elements that can be encoded:

▸ We have to chose bit number n so as to have enough space for encoding all our different


We can encode our elements as such:


▸ Problem (Mathematical Calculations) can be formulated differently

▸ Prob1 : use each number ones

▸ Prob2 : use each operator ones

▸ Prob3 : use each number ones, but there is no restriction about operators

▸ Prob4 : use operator ones, but there is no restriction about numbers

▸ There may be Restrictions on number of elements or Gene. It defines the length of chromosomes.

▸ Chromosome Length = Gene Length x Number of Genes


TargetGenes

Chromosome


‣We need to decide some parameters beforehand.

‣ Later according to performance, we have to change these parameters to improve the performance considering our restrictions such as

‣ Convergence ratio

‣ Computation number

‣ Computation types

‣ Storage, RAM, time, error tolerance (fitness score and target)…


GA DESIGN PARAMETERS

‣Number of POPULATION

‣ Length of CHROMOSOME

‣Value and type of CROSSOVER

‣Value of MUTATION RATE

‣Number of GENERATIONS (stop criteria)

‣ Type of SELECTION


‣Population (parents, fathers and mothers)

‣Population number can be 500, 20.000, or 1.000.000

‣We create randomly


‣We test each population member for a possible solution to the problem.

‣We decode each gene with the order.

‣Calculate the result mathematically ( 8 + 5 – 3 / 2 *4 / 7 = 2.857)

‣We need to define a Fitness Score for each member of population in every generation.

‣ It is normalized to [0 , 1].


▸ Fitness Score Calculation or Fitness Function

▸ There can be different calculations

▸ One of them is

|Target Value - Result|1_____________________


‣After assigning a Fitness Score to each population, we have to eliminate weak ones as nature does due to Darwin’s Natural Selection Principle

‣We need a Selection Pattern

‣One method is Roulette Wheel


‣ We define a crossover pattern.

‣ In nature, we have dominant and recessive genes.

‣ We model with random numbers.

‣ The aim of crossover is to sustain different members, chormosomes or solution variables.

‣ If the same population we cannot find the solution.

‣ We have to mix population by Crossover


‣We define a mutatation pattern

‣As it is also rare in nature, we have low mutation ratio

‣ The aim of mutation is sustain different members as well


THANKS FOR LISTENING… ANY QUESTIONS ?


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