After scientists became disillusioned with classical and neoclassical attempts at modeling intelligence, they looked in
Two prominent fields arose, connectionism (neural networking,
parallel processing) and evolutionary computing.
It is the latter that this essay deals with - genetic algorithms
and genetic programming.
Fuzzy logic is a form of many-valued logic
A Fuzzy Genetic Algorithm (FGA) is considered as a GA that
uses fuzzy logic based techniques
Definition of fuzzy
Fuzzy – “not clear, distinct, or precise; blurred”
Definition of fuzzy logic
A form of knowledge representation suitable for
notions that cannot be defined precisely, but which
depend upon their contexts.
Compared to traditional binary sets fuzzy logic
variables may have a truth value that ranges in
degree between 0 and 1
The membership function represents the
degree of truth as an extension of valuation.
The term "fuzzy logic" was introduced with
the 1965 proposal of fuzzy set theory by
Lotfi A. Zadeh.
Fuzzy logic has been applied to many fields,
from control theory to artificial intelligence.
Fuzzy logics however had been studied
since the 1920s as infinite-valued logics
notably by Łukasiewicz and Tarski.
A point on that scale has three "truth values"—one for each of the
red arrow points to zero, this temperature may be interpreted as
The orange arrow (pointing at 0.2) may describe it as "slightly
The blue arrow (pointing at 0.8) "fairly cold"
A genetic algorithm (or GA) is a search technique used in
computing to find true or approximate solutions to
optimization and search problems.
Genetic algorithms are categorized as global search heuristics.
Genetic algorithms are a particular class of evolutionary
algorithms that use techniques inspired by evolutionary
biology such as inheritance, mutation, selection, and
crossover (also called recombination).
The new population is then used in the next iteration of the
Commonly, the algorithm terminates when either a maximum
number of generations has been produced, or a satisfactory
fitness level has been reached for the population.
If the algorithm has terminated due to a maximum number of
generations, a satisfactory solution may or may not have been
• The evolution usually starts from a
population of randomly generated
• Individual solutions are selected through
a fitness-based process
• This generational process is repeated
until a termination condition has been
• improve the solution through repetitive
application of the mutation, crossover,
inversion and selection operators
The use of FL based techniques for either improving GA behaviour and
modeling GA components, the results obtained have been called fuzzy
genetic algorithms (FGAs),
The application of GAs in various optimization and search problems
involving fuzzy systems.
An FGA may be defined as an ordering sequence of instructions in which
some of the instructions or algorithm components may be designed with
fuzzy logic based tools
A fuzzy fitness finding mechanism guides the GA through the search
space by combining the contributions of various criteria/features that
have been identified as the governing factors for the formation of the
A single objective optimization model cannot serve the purpose of a fitness
measuring index because we are looking at multiple criteria that could be
responsible for stringing together data items into clusters. This is true; not
only for the clustering problem but for any problem solving using GA that
involves multiple criteria. In multi-criteria optimization, the notion of
optimality is not clearly defined. A solution may be best w.r.t. one criterion
but not so w.r.t. the other criteria. Pareto optimality offers a set of nondominated solutions called the P-optimal set where the integrity of each of
the criteria is respected.
The algorithm has two computational elements that work together.
i) The Genetic Algorithm (GA) and
ii) The Fuzzy Fitness Finder (FFF).
Cossover is a genetic operator used
to vary the programming of a
chromosome or chromosomes from
one generation to the next. It is
analogous to reproduction and
biological crossover, upon which
genetic algorithms are based. Cross
over is a process of taking more than
one parent solutions and producing a
child solution from them.
Mutation is a genetic operator used to maintain genetic diversity
from one generation of a population of genetic algorithm
chromosomes to the next.
It is analogous to biological mutation. Mutation alters one or
more gene values in a chromosome from its initial state.
In mutation, the solution may change entirely from the previous
solution. Hence GA can come to better solution by using
Mutation occurs during evolution according to a user-definable
This probability should be set low. If it is set too high, the search
will turn into a primitive random search.
· A genetic representation for
potential solutions to the problem.
While the population of the genetic
algorithm undergoes evolution at
every generation, the relatively
‘good’ solutions reproduce while the
relatively ‘bad’ solutions die.
· Method to create an initial
population of potential solutions
To distinguish between solutions, an
objective (evaluation) function is
used. In the simple cases, there is
only one criterion for optimization
for example, maximization of profit
or minimization of cost.
· Selection of individuals for the next
But in many real-world decision
making problems, there is a need for
simultaneous optimization of
· An evaluation function to rate
solutions in terms of their “fitness”
· Genetic operators that alter the
composition of the children
In order to make a successful run of a
GA, the values for the parameters of
the GA have to be defined like the
population size, parameters for the
genetic operators and the terminating
• The Fuzzy Fitness Finder
• Input and Output Criteria
• Fuzzification of Inputs
• Fuzzy Inference Engine
• Defuzzification of Output
Iterative Rule Learning Approach
The Nagoya Approach
Approx. in all sectors of life.