CONTENTS
REPORT ON:
GENETIC ALGORITHM
SUBMITTED TO:
SUBMITTED BY:
PROF. K.K.TRIPATHI
MAITREE RASTOGI RD
EC-C 3 YR.
ACKNOWLEDGEMENT I have taken efforts in this project. However, it would not have been possible without the kind support and help of many individuals and organizations. I would like to extend my sincere thanks to all of them. I am highly indebted to Prof. K.K. Tripathi for his guidance and constant supervision as well as for providing necessary information regarding the project & also for their support in completing the project. I would like to express my gratitude towards my parent for their kind co-operation and encouragement which help me in completion of this report. My thanks and appreciations also go to people who have willingly helped me out with their abilities.
TABLE OF CONTENTS 1.
INTRODUCTION
2.
WHAT IS GA?
3.
HOW GA WORKS?
4.
KEYPOINTS
5.
APPLICATIONS
6.
ADVANTAGES
7.
SHORTCOMINGS
8.
CONCLUSION
9.
REFERENCES
INTRODUCTION The USE of genetic algorithms (GA) for problem solving is not new. The pioneering work of J. H. Holland in the 1970’s proved to be a significant contribution for scientific and engineering applications. Since then, the output of research work in this field has grown exponentially although the contributions have been, and are largely initiated, from academic institutions world-wide. It is only very recently that we have been able to acquire some material that comes from industry. The concept of this is somehow not clearly understood. However, the obvious obstacle that may drive engineers away from using GA is the difficulty of speeding up the computational process, as well as the intrinsic nature of randomness that leads to a problem of performance assurance. Furthermore, the GA is not considered a mathematically guided algorithm. The optima obtained is evolved from generation to generation without stringent mathematical formulation such as the traditional gradient-type of optimizing procedure. In fact, GA is much different in that context. It is merely a stochastic, discrete event and a nonlinear process. The obtained optima is an end product containing the best elements of previous generations where the attributes of a stronger individual tend to be carried forward into the following generation. The rule of the game is “survival of the fittest will win.”
Reproductio
Survive
Competition
Selection
The genetic algorithm differs from a classical, derivative-based, optimization algorithm in two main ways, as summarized in the following table.
Classical Algorithm
Genetic Algorithm
Generates a single point at each iteration. The sequence of points approaches an optimal solution.
Generates a population of points at each iteration. The best point in the population approaches an optimal solution.
Selects the next point in the sequence by a deterministic computation.
Selects the next population by computation which uses random number generators.
What Are Genetic Algorithms? Genetic algorithms (GAs) are problem solving methods (or heuristics) that mimic the process of natural evolution. Unlike artificial neural networks (ANNs), designed to function like neurons in the brain, these algorithms utilize the concepts of natural selection to determine the best solution for a problem. As a result, GAs are commonly used as optimizers that adjust parameters to minimize or maximize some feedback measure, which can then be used independently.
GA mimics all the processes based on the concept of natural evolution to find the optimum solution to the given problem residing in the search space. The GA pool contains a number of individuals called chromosomes. Each chromosome encoded from the parameters holds the potential solution. According to the evolutionary theories, the chromosomes which only have a good fitness are likely to survive and to generate the offspring and pass its strength to them by the genetic operator. How Genetic Algorithms Work Genetic algorithms are created mathematically using vectors, which are quantities that have direction and magnitude. Parameters for each trading rule are represented with a one-dimensional vector that can be thought of as a chromosome in genetic terms. Meanwhile, the values used in each parameter can be thought of as genes, which are then modified using natural selection.
For example, a trading rule may involve the use of parameters like Moving Average Convergence-Divergence (MACD), Exponential Moving Average (EMA) and Stochastic. A genetic algorithm would then input values into these parameters with the goal of maximizing net profit. Over time, small changes are introduced and those that make a desirably impact are retained for the next generation.
There are three types of genetic operations that can then be performed:
Crossovers
Represent the reproduction and biological crossover seen in biology, whereby a child takes on certain characteristics of its parents. Single point crossover
Two point crossover (Multi point crossover)
Mutations
Represent biological mutation and are used to maintain genetic diversity from one generation of a population to the next by introducing random small changes.
Selections
Are the stage at which individual genomes are chosen from a population for later breeding (recombination or crossover).
These three operators are then used in a five-step process: 1. Initialize a random population, where each chromosome is nlength, with n being the number of parameters. That is, a random number of parameters are established within elements each. 2. Select the chromosomes, or parameters, that increase desirable results (presumably net profit). 3. Apply mutation or crossover operators to the selected parents and generate an offspring. 4. Recombine the offspring and the current population to form a new population with the selection operator. 5. Repeat steps two to four. Over time, this process will result in increasingly favourable chromosomes (or, parameters) for use in a trading rule. The process is then terminated when a stopping criteria is met, which can include running time, fitness, number of generations or other criteria.
APPLICATIONS Using Genetic Algorithms in:
Trading
While genetic algorithms are primarily used by institutional quantitative Traders, individual traders can harness the power of genetic algorithms - without a degree in advanced mathematics - using several software packages on the market. These solutions range from standalone software packages geared towards the financial markets to Microsoft Excel add-ons that can facilitate more hands-on analysis. When using these applications, traders can define a set of parameters that are then optimized using a genetic algorithm and a set of historical data. Some applications can optimize which parameters are used and the values for them, while others are primarily focused on simply optimizing the values for a given set of parameters.
VLSI
A genetic algorithm for the physical design of VLSI-chips is presented. The algorithm simultaneously optimizes the placement of the cells with the total routing. During the placement the detailed routing is done, while the global routes are optimized by the genetic algorithm.
FILTER DESIGN
Morphological filters are an important class of non-linear digital signal processing and analysis filters, having found a range of
applications, giving excellent results in areas such as noise reduction, edge detection and object recognition. However, design methods existing for these morphological filters tend to be computationally intractable or require some expert knowledge of mathematical morphology.simple genetic algorithms can be employed in the search for optimum morphological filters for specific signal/image processing tasks.
Robotics
The application of GA in robotics has found a use in robot navigating systems. Considering that such navigation is the art of directing a course for a mobile robot to traverse in a restricted environment, this navigation scheme should then be designed to cope with such constraints, so that the robot is capable of reaching its desired destination without getting lost or crashing into any objects.
Game Playing
Genetic algorithms to find the solution of game theory. We proposed new method for solving game theory and find the optimal strategy for player A or player B.
MATLAB
You can apply the genetic algorithm to solve problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, non-differentiable, stochastic, or highly nonlinear. A GA Toolbox was developed for MATLAB [87]. This is GA software which is easy to use, practical, and efficient. It provides a platform for modelling, design, and simulation with an interactive environment and the associated graphical facility.
FACE DETECTION
A Face-print system was designed in New Mexico State University [for reproducing the feature of a suspected criminal’s face. A binary chromosome is used to code the five facial features (mouth, hair, eyes, nose, and chin) of a face. Initially, 20 suspects’ faces are generated on a computer screen. A witness can then rate each face on a 10-point subjective scale. GA takes that information and follows the evolution cycle to obtain the optimal solution
Control
For its use in control systems engineering, GA can be applied to a number of control methodologies for the improvement of the overall system performance. In most of the controller designs, some parameters are required to be optimized in order to give a better overall control performance. Furthermore, the configuration or the order of the controller may be optimized to reduce the system complexity.
Speech Recognition
In an automatic speech recognition system, the spoken speech patterns (test pattern) are usually identified with the pre stored speech patterns (reference patterns). The comparison of speech signals has a number of difficulties as variations in time and the time scales among them are not fixed. Therefore, time registration of the test and the reference patterns is one of the fundamental problems in the area of automatic isolated word recognition.
ADVANTAGES OF GA: Some insight into why GA has become more and more popular should be given. GA is attractive for a number of reasons.
It can handle problem constraints by simply embedding them into this chromosome coding. Multi-objective problem can be addressed by means of the approach. Since it is a technique independent of the error surface, it is ready to solve multimodal, non-differentiable, non-continuous, or even NP-complete problems. Structured GA provides a tool to optimize the topology or the structure in parallel with the parameters of the solution for a particular problem. It is a very easy-to-understand technique with very few (or even none) mathematics. It can be easily interfaced to existing simulations and models.
SHORTCOMINGS OF GENETIC ALGORITHM Of course, there are some things that GA just cannot or finds difficult to do. The following three phenomena are often encountered.
Deception Some objective functions may be very difficult to optimize by GA. Such functions are referred as GA-deceptive functions.
Genetic Drift (Bias) There is no guarantee of obtaining the global optimal point by using GA although it has the tendency to do so. This possibility is reduced if there is a loss of population diversity. As CA seeks out a suboptimal point, the population may converge toward this point and premature convergence occurs. In this case, the global optimal solution can only be obtained by the exploration of mutation in the genetic operations. Such a phenomenon is known as genetic drift.
Real-Time and On-Line Issues Similar to the other artificial intelligence (AI) techniques and heuristics, the GA is not suited for analyses that would provide guaranteed response times. Moreover, the variance of the response time for a GA system is much larger than the conventional methods. This unfavourable nature limits the use of GA in the real-time problem.
CONCLUSION An attempt to outline the features of GA in terms of the genetic functionality of operators, the problem formulation, the inherent capability of GA for solving complex and conflicting problems, as well as its various practical applications, is given in this paper. The theme is oriented from an industrial application basis. The paper’s purpose is to introduce this emerging technology to engineers who may have little or no knowledge of CA. It is also reasonable to believe that this article contains sufficient, useful material to encourage and awaken the interest to newcomers, so that GA may be implemented to solve their practical problems. What remains as a challenge to GA is undoubtedly the real- time and adaptive capability. The real-time issue is not so much hinged on the computing speed, since the parallelism of GA should improve the computational speed quite considerably and comfortably. The integration of CA with other emerging technology such as neural networks and fuzzy logic systems could be another challenging area. The combination of these emerging technologies may not only involve applying GA as a helper to these two, but could result in the emerging technologies being able to assist GA applications. Different combinations may offer us a fruitful result in intelligent system design. All in all, the knowledge generated from GA over the last 20 or so years has now become mature. The prospect of applying GA for practical applications is good. A considerable growth in the application GA, particularly in the field of industrial electronics, is anticipated for the future.
REFERENCES 1) www.wikipedia.com 2) www.google.com 3) www.scribed.com 4) www.investopedia.com 5) www.mathworks.com 6) www.sciencedirect.com 7) www.ieee.org
