Introduction to genetic algorithm
Prepared by
M .U. Kale
Assistant professor
Irrigation & Drainage Engineering
Dr. P.D.K.V. Akola
What is a Genetic Algorithm (GA)?
Genetic Algorithm It is search procedure based on
the mechanics of natural
selection and natural genetics
i.e. survival of the fittests.
Optimization
 Optimization is the selection of a best element (with
regard to some criteria) from some set of available
alternatives
 More generally, optimization includes finding "best
available" values of some objective function given a
defined domain, including a variety of different types
of objective functions and different types of domains.
Potential Benefits of applying optimization
to water resource problems
 Cost minimization
 Benefit maximization
 Increase Power,
 minimum loss
 maximum release etc.
Linear Programming (LP)


LP is common in economy and is meaningful only if it is with
constraints.
A is p × N and has
Two forms:
full row rank (p<N)
1. Minimize
subject to:
2.


Minimize
subject to:
If the LP minimizer exists it must be one of the vertices of the
feasible region.
A fast method that considers vertices is the Simplex method.
Evolutionary Algorithm
 Evolutionary
algorithms
(EAs)
is
population-based
metaheuristic optimization algorithms that use biologyinspired mechanisms like mutation, crossover, natural
selection, and survival of the fittest in order to refine a set
of solution candidates iteratively.
 The advantage of evolutionary algorithms compared to
other optimization methods is their “black box” character.
 EAs perform consistently well in many different problem
categories.
Classification of Evolutionary Algorithms
1. Genetic Algorithms (GA)
2. Genetic Programming (GP)
Genetic algorithms (GA)
 GAs are a subclass of evolutionary algorithms where
the elements of the search space G are binary
strings
(G = B∗) or arrays of other elementary
types.
 The genotypes are used in the reproduction
operations whereas the values of the objective
functions f, ∈, F are computed on basis of the
phenotypes in the problem space X which are
obtained via the genotype-phenotype mapping
“gpm”.
 GAs subsume all evolutionary algorithms which have
bit strings as search space G.
.
Genetic Programming (GP) :
 GP includes all evolutionary algorithms
that grow programs, algorithms, and
similar constructs.
 All EAs that evolve tree-shaped individuals
are instances of Genetic Programming.
Application of GA in water resources
 Pipe network
 Design of networks, and analysis
 Ground water management problems
 Quantity and quality management models
 Reservoir operation
 Single purpose single reservoir
 Multi-purpose single reservoir
 Multi-reservoir systems
 Multi-purpose multi-reservoir systems
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Advantages of GA model
1. The GA typically uses a coding of the decision
variable set, not the decision variable itself.
2. The GA searches from a population of decision
variable sets, not a single decision variable set.
3. The GA uses the objective function itself not the
derivative information.
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Advantages of GA model
4.The GA algorithm uses probabilistic (not deterministic)
search rules.
5.GA takes care of stochasticity also.
6.GA does not requires discretization of state variables.
7.GA does not requires transition probabilities.
8.GA does not have curse of dimensionality problems.
9.GA models results in optimal or near optimal solutions.
Disadvantages of GA model
1. Cannot handle large number of constraints like LP models.
2. Computationally difficult to provide very long string length with
binary coding
- To some extent overcome by hexagonal coding.
3. Every iteration need objective function evaluation.
4. Difficult to handle mutation.
Besides these disadvantages, GA model still provide better
solutions than the conventional optimization techniques in
developing reservoir operating rules.
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