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Introduction of
Genetic algorithms vs neural networks
The Genetic
Algorithm is search heuristic which is inspired by the theory of Darwin of the natural
evolution. The genetic algorithm reflects the process of the selection of the
fittest element naturally which is selected to reproduce the descendants of the
next generation. In this report, the genetic algorithm, as well as neural
network, are discussed which are providing very informative information on what
kind of problems can be solved by these algorithms. Furthermore, the report is
also providing information on how such kinds of algorithms can be effective to
solve the problems. The genetic algorithms are really effective in using the
neural network to resolve any kind of issue in the neural network. Furthermore,
the studies are also describing very important information that how these algorithms
can be effective. In the last of this report, a brief analysis or comparison is
also provided between the genetic algorithm and the neural network.
Genetic Algorithm
and Neural Network
The genetic
algorithm is a significant method of heuristic searching in computer science
and Artificial Intelligence. The algorithm is very effective as well as very useful
because it can find the improved solution to search the problem which is based
completely on the natural selection theory as well as the biology of evolution.
Furthermore, such kind of algorithms is very useful and effective for searching
by using the complicated as well as larger data sets. On the other hand, the
neural algorithms are different than the genetic algorithms which refer to the
set of algorithms the neural networks are designed to determine and identify
the patterns
Why these
algorithms are used of Genetic algorithms vs neural networks
The genetic
algorithms are completely based on finding methods using heuristics which is
completely used in artificial intelligence because it is very intelligent
technology into the computer science field. The main reason to use such kind of
algorithms is to generate the solutions for occurred issues in the computer
systems with high quality to optimize as well as identify the problems through
depending on the biologically inspired operators like selection, mutation as
well as a crossover.
On the
other side, the neural network which is based on the set of different types of
algorithms used to solve several kinds of problem-related to the business and
other problems such as validation of data, sales, customer research,
forecasting as well as the risk management.
What type of
problems can be solved by these?
Several kinds
of problems can be solved by using genetic algorithms because genetic
algorithms can behave like artificial intelligence. It is because the genetic
algorithms are effectively used in artificial intelligence. The problems which
can be solved by the genetic algorithms are also provided in this document. The
very significant problems which are solved by this algorithm are: to check the
ambiguity in the system or function as well as the problems in the evaluation
processes. Furthermore, the genetic algorithm can also be used to solve time-related
problems to optimize as well as they are also helpful to find and provide the best
ever solutions for expensive processes. Mostly the genetic algorithms are used
to solve engineering-related problems. The Genetic algorithms can also be used
most of the times as the problem-solving techniques to resolve the global
optimization problems. Some examples are also provided in this document related
to the problems which are solved by such kind of algorithms
The mirrors designed to funnel sunlight to a solar
collector, walking methods for figures of computer, antennae designed to pick
up the radio in space as well as the optimal design of bodies of aerodynamic in
complicated flow field are the significant examples of solving by genetic
algorithms.
Use of Genetic
Algorithm on Neural network
The neural
networks attached to the genetic algorithms which can very useful to speed up
the learning process to solve the actual problem. The genetic algorithms, as
well as the neural nets, are now being used by all of the big companies to
provide help to the neural networks to learn effectively and efficiently. Furthermore,
in the use of genetic algorithms with the neural networks, the genetic
algorithm generates several kinds of significant solutions to the provided
problem as well as they are also evolved by using the various generations. Every
solution against the problem holds all types of parameters. Furthermore, those
solutions can also provide help to increase the resulting power. Although, all
weights in the artificial neural networks will be contained by the single solution
in the genetic algorithm 
If the population has
also eight solutions with almost 24,540 parameters per solution and the total
population will be .
The overwhelming use of
genetic algorithms is in the optimization problems. in the optimization
problems genetic algorithms provide maximize and minimize objective function
with the value under the set of constraints.
the common usage of genetic algorithms is to generate high quality
solution for the optimization of problems, and it depends on the biologically
inspired operators including selection, crossover, and mutation. The proper
usage of genetic algorithm provides five features. The possible encoding
solutions of the problems consider the individuals in the selected population.
The selection process is divided in the series and the small steps are the
building blocks. The process represents genes and series of genes. The
artificial neural network and the genetic algorithm are considered as great
source of inspiration for the mankind. The GSs working and selection is based
on the algorithms, genetics, and natural selection. The Gas are the subsets of
large branch of computation and this computation is known as Evolutionary
computation. The best alternative work condition for the neural network is to
work over the genetic algorithm. The genetic programming does not consider
conditions and working of optimization of genetic algorithms.
Algorithm for
weight matrices of Genetic algorithms vs neural networks
Comparison among
Genetic Algorithm and Neural Network
In the
comparison of a genetic algorithm as well as the neural network, the neural network
is the method or the technique which is used to describe the mapping and the
genetic algorithm is the process of optimization.
The genetic
algorithm is a method for the optimization of random numerical. It means that the
genetic algorithm provides the parameters to optimize the specific function. The
algorithm starts with many chosen parameters randomly as well as hold a set of
different types of the parameter which are very beneficial and give the very
low loss. On the other side, the artificial neural networks are the set of
multiple algorithms to provide the information on the trainable function which
is also known to perform a wide variety of tasks. In the context of the neural
networks, the analogy biologically is also considered as the functioning of
brains
Conclusion of
Genetic algorithms vs neural networks
It is concluded that the genetic algorithm is a significant method of heuristic searching in computer science and Artificial Intelligence. Such kind of algorithms are very useful and effective for searching by using the complicated as well as larger data sets. The neural network which is based on the set of different types of algorithms used to solve several kinds of problem-related to the business and other problems such as validation of data, sales, customer research, forecasting as well as the risk management. The mirrors designed to funnel sunlight to a solar collector, walking methods for figures of computer, antennae designed to pick up the radio in space as well as the optimal design of bodies of aerodynamic in complicated flow field are the significant examples of solving by genetic algorithms. Every solution against the problem holds all types of parameters. Furthermore, those solutions can also provide help to increase the resulting power.
Abdella, M., & Marwala, T. (2005). The use of
genetic algorithms and neural networks to approximate missing data in database.
. In IEEE 3rd International Conference on Computational Cybernetics,
207-212.
Chow, T. T., Zhang, G. Q., Lin, Z., & Song, C. L.
(2002). Global optimization of absorption chiller system by genetic algorithm
and neural network. . Energy and buildings, 103-109.
Erenturk, S., & Erenturk, K. (2007). Comparison of
genetic algorithm and neural network approaches for the drying process of
carrot. Journal of Food Engineering, 905-912.
Gupta, J. N., & Sexton, R. S. (1999). Comparing backpropagation with a genetic algorithm for neural network training. Omega, 679-684.
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