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Genetic algorithms and the overall evaluation process of solving optimization problems
I. Introduction
The development and use optimization models is well established. However, the use of many models has been restricted in certain areas of economic analysis where the problem is vast and many nonlinear interactions. In most cases, the use linear approximations or simplification of the model was necessary to obtain a solution. GA follows the concept of evolution of the solution stochastically developing generations of solution populations with a given fitness statistic. Particularly applicable to problems that are not large, linear and possibly discrete in nature. Genetic algorithms which may explore a much wider range of possible solutions to a problem not
conventional programs. It is designed to simulate natural processes necessary for the evolution of the system, especially those who follow the principles of survival of the fittest [1]. As such, they represent a wise use of a random search within a search space is defined to solve a problem. The genetic algorithm implicit parallelism allows it to test and operate a large number regions in the search space while manipulating relatively few strings.
II. Genetic algorithms
Algorithms genetic search method based on probabilistic ideas of evolutionary processes. The GA procedure is based on the Darwinian principle of survival of the fittest. From an initial population is created with a predefined number individuals (or solutions), each represented by a gene chain (incorporating the variable information). Each individual has a measure of fitness partner, usually represents a target value. The concept that stronger (or more) individuals in a population to produce offspring is then implemented installation in order to reproduce the next population. People are selected for reproduction (or crossover) in each generation, with a factor of mutation appropriate random changes of individual genes, to develop the new population. The result is another set of individuals based on original themes that allow people with a subsequent better (min. or max.) fitness. Therefore, the algorithm to identify individuals with the optimization of fitness values, and those who have less natural ability of the excluded population.
Finally, this search procedure is a set of variables that optimizes the ability an individual and / or the entire population [2]. Consequently, the GA technique has advantages over traditional non-linear solution techniques can not always achieve an optimal solution. Comparison of the General Assembly and Technical simplified classical solution is illustrated in Figure 1. Generally not solved by linear programming using some form of search technique gradient to move along the steep slope to the highest point (maximization) is reached. In the case of linear programming, a global optimum always be attained. However, the linear programming models may be subject to problems of convergence to local optimum, or in some cases it may be able to find a viable solution. It depends largely on the starting point for the solver. A starting point outside the feasible region may give rise to any feasible solution to find, even if workable solutions May exist [3]. Other starting points may lead to an optimal solution, but not possible to determine whether a local or global optimum. Therefore, the designer can never be sure that the optimal solution that the model is "true" optimum.
Fig 1. Gradient search technique
Figure 1 (approach) a genetic algorithm
For the genetic algorithm, the population includes a range of possible outcomes. The solutions are identified on a purely physical local optima and therefore does not differ from others of the same dresser. These solutions closer to the global optimum so that you increase the value of fitness. Generations Successive improvements in the ability of individuals in the population until it meets the convergence criterion of optimization. Because GA is probabilistic nature tends to global optimum, but models for the same reasons, GA can not guarantee to find the optimal solution as shown in Figure 1 (a).
The General Assembly is composed of four main phases: evaluation, selection, crossover and mutation. Evaluation procedure measures the ability of each individual solution in the population and assigned a relative value based on the optimization of the definition (or research) criteria. Normally, in a scenario of non-linear programming, this measure reflects the objective value of the model. The procedure selection randomly chooses people from the current population for development of next generation. Several alternatives were proposed, but all follow the idea that the fittest are more likely to survive. The procedure takes two people crossing selected and combined on a crossover point thereby creating two new individuals. Simple (asexual) reproduction can also happen that the reply of one person in the new population. The transfer procedure randomly modifies the genes of a person subject to a factor of small change, introducing more randomness in the population.
This iterative process continues until one of the possible termination criteria is met: if there is a known solution optimal or acceptable, or a maximum number of generations have done, or if a number of generations without physical improvements occur [4]. In general, the last of those convergence criteria are applied to lower optimal solution.
Size of the selected population is probably the most important parameter, which reflects the size and complexity of the problem. However, the balance between the extra effort of calculating the population size is a problem specific to the decision to be set by the modeler, as doubling the size of the population will almost double time resolution for the same number of generations. Other parameters are the maximum number of generations to a probability of passing the probability of mutation, a method of selection and, possibly, an elitist strategy where the best are continuing to the next generation population.
Unlike traditional optimization methods, GA is better able to handle variables of type set of continuous variables. This is due to the inherent granularity of channels variable genes within the structure of the GA model. Normally, a variable that applies to a range of possible values of a binary string that indicates the number of these values, ie if x1 [0, 15] and chain genes is 4 characters (for example, "1010"), then there are 16 possibilities for research to consider. For this model as a continuous increase the number of possible values vary significantly [5]. Similarly, information of another variable that helps in research is significantly higher and lower limits. These factors may affect the convergence of solution patterns significantly.
III. TYPE OF ASSESSMENT PROCESS FOR OPTIMIZATION OF GA
In general, GA has the following elements: populations of chromosomes, selection according to fitness crossover to produce new offspring and random mutation of new offspring. Each chromosome can be a point in space research candidate solutions. The GA processes populations of chromosomes, successively replacing one of those people with one another. The General Assembly often requires more an objective function which assigns a score of fitness for each chromosome of the current population. The ability of a chromosome depends on how this chromosome solves the problem. The chart below. Figure 2 shows the typical evolution process of the General Assembly.
Fig.2 Typical evaluation process GA
The key issue is that evolution is a massively parallel (global) search method. Unlike the iterative local search such as Snake, who is a starting point and converges to a single result, a population chromosome is maintained in Georgia during the entire evolution and may converge to more than one peak in the solution space at the same time [6,7].
In our application, the chromosome is defined as $ 9 and some additional parameters help to rebuild a limit 9 pesos. Therefore, each chromosome represents a potential limitation that could be the solution to our problem. One can see that the prostate is a ultrasound image as a relatively dark. Therefore, the objective function is defined so that the fitness level is high given the range of potential that has the darkest area within and outside the region better. The limits, for example, chromosomes have the opportunity to mate and produce the next generation of chromosomes.
IV. Genetic Elements genetic algorithms
The algorithms must take these elements to be "genetic"
v representation mathematical solutions. This is a value chain. A value for a given position has a special meaning (as do the genes). Keeping the optimization problem of space in mind, we might have a string that represents the position of the 3 boxes x, y, z coordinates. For simplicity, each coordinate is represented by a decimal, so that position 1, tells us the x coordinate of box 1, position 2 are the coordinates of box 1 and position 3 is the z coordinates of the box 1. Position 4, then the coordinates of the box 2, etc. It could be something like (Channel 9 digits is the "chain of genes" that represents a specific solution)
v A method of creating the population original. You determine how many people you want. If you have an idea of the best individuals to initialize accordingly. However, most of the time it is probably better to sample random values.
v A method for measuring fitness. You must have a measure of the ability to select the best individuals. In this example, the obvious measure is the amount of free settlement offer proposed. The more free space, the more the solution.
gene functions V. It is the selection, crossover and mutation methods mentioned above. I'll explain below, is sufficient now to say that existing individuals are recombined in new people who represent new solutions.
A number of parameters. You must determine in advance the size of the population, number of parents to choose the mutation rate, etc.
This diagram illustrates the basic steps in a GA as shown in Figure 3.
Initialize
People
People
Calculate
Fitness
Download
Solution
Found?
The Offspring
Genetics
Operations
Stop
Iterations
Fig 3. Milestones in GA
V. Applications of genetic algorithms
(A). The General Assembly optimization and planning: Traveling Salesman Problem (TSP)
The TSP is interesting not only from a theoretical standpoint, many practical applications can be modeled as a traveling vendor problem or its variants, for example, the movement of the pen of a plotter, drilling of printed circuit (PCBs), conducting the real world of school buses, airlines, delivery trucks and postal companies. Researchers have identified TSPs to examine means of the bimolecular process of a parallel processing computer networks ", to advance in cryptography, to determine the order of thousands of exposures needed in X-ray crystallography to determine the paths of fires in forest research (which is a supplier issue several partitions on a single TSP). Therefore, there is enormous need algorithms. Over the past two decades, enormous progress has been made on Trip to the solution of optimization problems that seller, of course, is the ultimate goal of every researcher. One of the milestones in research optimal solutions is a problem in 3038-the city. This progress is due in part to the rise of computers [8]. Especially, it was possible by the development of mathematical theory and efficient algorithms. There are strong relations between the restrictions the problem representation adopted and the genetic operators that can be used with it. The purpose of travel vendor problem is to design a plan of travel (one trip), which minimizes the total distance traveled. TSP is NP-hard (NP means time is not deterministic polynomial time) – is generally thought can not be solved (exactly) in polynomial time. The TSP is limited;
v The seller can only be in town at any time
v The cities must be visited once and only one hour.
When genetic algorithms applied to problems of very large, not in two respects: They scale very bad (in terms of time complexity) that increases the number of cities. The solution quality degrades rapidly. To use a standard GA issues following must be resolved:
v A binary representation Travel is so you can easily translate into a chromosome.
v A function appropriate adaptation are designed, taking into account limits.
vn permutation matrices are unrealistic solutions, For example, the GA can generate solutions that do not represent valid chromosomes. This happens: in the initialization step randomly the GA. following genetic operators (mutation and crossover). Thus, permutation matrices are used. Two visits, including the same Cities in the same order but with different starting points or different orientations are represented by different matrices and thus different chromosomes, for example:
Tower (23541) = Round (12354)
(i) Evolution Divide and Conquer (EDAC)
This approach, CTS has the potential problem for any research in which knowledge of good solutions to the problems of submarine can be exploited to improve the solution the problem itself. The idea is to use genetic algorithms to explore the space of problem subdivisions rather that the solution space for themselves, and thus exploit the qualities of the scale, almost inherent in the general linear approach "divide and conquer. In addition, Micrel's approach is inherently parallel. The approach of the EDAC has applied the GA to FST an order or greater importance in terms of size of problem representations of permutation. Experimental results show the properties Micrel success for uniform random points and problems of PCBs in the range 500 – 5000 cities.
(B). GA in Business its role and support decision making
Genetic algorithms were used to solve various problems Business in functional areas such as finance, marketing, information systems and the Production / s. operating within these functional domains, genetic algorithms has a variety of applications such as tactical asset allocation, scheduling, reunion machine part, and the design of computer networks.
Models of tactical asset allocation and international equity strategies have improved with the use of genetic algorithms. They reported an improvement of 82% portfolio value accumulated in a passive reference model and improvement of 48% on a model of GA Not designed to improve the passive reference. Genetic algorithms are particularly well suited for applications in modeling Financial.
(ii) Production / Operations Applications
Genetic algorithm was used to schedule tasks in an installation depends on a sequence of minimum total delay. All works are programmed on a single machine, each job has a processing time and an expiration date. The preparation time for each job depends on the task before it. GA is able to find the right one, but not necessarily optimal schedules, quite quickly. It is also used to schedule tasks in the environment does not depend on the configuration script. Work is scheduled on a machine with the objective of reducing the overall total weighted earliness penalty or delay the due dates of jobs. However, this does not guarantee that it will generate optimal solutions for all zones. It was developed to solve the machine grouping problem required component for cellular manufacturing systems. Provides a set of satisfactory solutions to environmental two objectives, allow the decision and then select the best alternative.
(iii) - Role in decision decision
Apply well-established model of treatment decision phase of Simon (1960), genetic algorithms seem to be very adequate to support the design and choice phases of decision making. To solve a single objective problem, designs of many GA solutions no improvement (no increase in fitness) can be achieved or a specified number of generations have evolved, or when the processing time allocated is complete. The solution being in the last generation is that which maximizes or minimizes the objective (fitness) function, this solution can be thought of as recommended by the election. Therefore, the single objective problems that the user of GA is assisted in the selection phase of decision processing. When the solution of many problems of late, which allows many satisfactory solutions in terms of objectives, and enables decisions to choose the best alternative. Therefore, assistance in the design phase of the process problems Decision with multiple objectives. Its can be very useful for the consideration of alternatives from which they are designed to evaluate possible solutions and generate new (and better) solutions for evaluation. Therefore, it can improve the quality of decision making.
(C). Robot Behavior Learning Genetic Algorithms
Robot has become as a tool increasingly important who has taken a larger role in many different industries. As such, it must operate with high efficiency and precision. This may not seem very difficult if the environment in which the robot operates has not changed since the robot's behavior can be pre-programmed. However, if the environment constantly changing, it becomes extremely difficult or impossible for programmers to explore all possible behaviors the robot. The application of robots in a changing environment is not only inevitable in modern technology, but also increasingly frequent. This has obviously led to the development of a robot learning.
The approach to learning behaviors leading the robot to its goal, reflected by a particular methodology Learning through simulation modeling. The motivation is that the decision errors in the real system can be costly and dangerous. In addition, the constraints of time may limit the degree of learning in the real world. Since learning requires experimentation with behaviors that can sometimes produce undesirable results if applied in the real world. Therefore, as shown in the diagram, the current behavior may be best place in the real world, the online system, while still learning the system off-line.
(D). Advantages and disadvantages of the algorithms genetic
The GA has a number of advantages. It can quickly scan a whole range of solutions. Bad proposals do not interfere not the final solution because they are simply discarded. The inductive nature of the GA means that there are rules about the problem – Working for their own internal regulations. This is very useful for complex or poorly defined problems. Nature of parallel stochastic search is one the major advantage of GA genetic approach has drawbacks, of course.
While the great advantage of GA is the fact that finding a solution Thanks to evolution, it is also the biggest disadvantage. Evolution is inductive in the life of nature evolving into a good solution – change away from bad circumstances. This can cause a species to evolve in an evolutionary cul de sac. GA is usually slower than traditional techniques.
Consider this example: a GA to find the highest point in a landscape. The algorithm to favor solutions that are at a point (a hill or other). What people are gradually beginning to offer solutions in the area (somewhere in the draft law), are beginning to resemble. Ultimately, it is possible that people who are almost identical. The best of these suggest the summit of the peak as the solution. But what if another higher peak at the other end of map? It is very difficult for people at risk from their peak current. Those who will not be eliminated, because their solution is worse than we have. A person can "break" but it would mean that their "genes" are very different from the rest of the population, so this is unlikely. In other words, the algorithm gives a suboptimal solution – and not even know.
VI. CONCLUSION
GA is an evolutionary approach is an alternative to traditional optimization methods. It is best suited for the nonlinear model complex instead of the global optimum is a difficult task. It may be possible to use certain techniques to study problems that can be modeled more accurately. Genetic approaches are often simple in design and easy to code, and can be used to increase significantly the probability of finding the truly global optimum multiple functions. We argue that a thorough analysis of mechanisms development is useful to understand the success of several standard techniques, can clarify the relationships between more recently proposed improvements.
Applications, whether commercial, educational and scientists are increasingly dependent on this algorithm, genetic algorithms. Its usefulness and through the resolution of problems has been the favorite choice among traditional methods, namely the gradient search, random search and others. It is very useful when the developer has no experience in accurate, because he has the ability to explore and learn their area. In the future, would see some changes in variants of the AG to the extent for some very specific tasks. In general, a standard genetic algorithm is taken to develop specific research problem in the model must take advantage of the structure type of application effective. A number of factors to consider in developing a model of genetic algorithm, it is generally many standard parameters which can change to affect the performance specification variable optimization (deterministic or probabilistic), tight the limits of variable weighting strategies and limitations Finally, the successful implementation and smooth transition from one model in a GA is directly related to the knowledge of the modeler in three subjects, programming models and understanding the basics of the GA. It is advantageous to spend time to create an effective model with variables (some) control variable in an area largely feasible.
VII. References
[1]. Rajasekaran, Vijayalakshmi, S. 2005 Rajasekaran, GAVijayalakshmi, neural networks, fuzzy logic and genetic algorithms for synthesis and applications Prentice Hall de Mexico SA. Ltd., 2005 (5th edition).
[2]. Patterson, 2003 Dan W. Patterson, Introduction to AI and Expert System Prentice Hall of India Pvt Ltd.., New Delhi, 2003.
[3]. Goldberg, 1989 Goldberg, DE, Genetic algorithms in search, optimization and machine learning, Addison-Wesley, USA, 1989.
[4]. Louis J. 1993Sushil Louis Algorithms DNA as a tool for calculating Design, August 1993.
[5]. Brooke, Brooke 1998, AD, D. Kendrick and A. Meerhaus, GAMS: A User's Guide, Science Press, 1988.
[6]. Carroll, 1997 Carroll, DL, Fortran GA – Genetic Algorithm v1.6.4 Driver Guide user, 1997.
[7]. Baeck, 1998 Baeck, T. a user guide to 1.0 GENESYS, Department of Computer Science, University of Dortmund, 1998.
G. Winter, J. Mr. & Periaux Galan Genetic algorithms computer engineering.
[8]. Darrell Whitley, Vose, 1995 L. Darrell Whitley and Michael D. Vose, genetic algorithms Foundatiions Volume 3
Morgan Kaufmann Publishers, Inc., 1995.
About the Author
Assistant professor in lord venkateswara engineering college.I am doing phd in sathyabama university, Tamil Nadu,India.shankar_submanian@yahoo.co.in
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