Modern Heuristic Methods - Report Example

Modern Heuristic Methods (MHM) Table of Contents Introduction A heuristic method is utilized to quickly get a solution that is expected tobe close to the possible answer, or optimal answer’. In detail, a heuristic is a rule-of-thumb, a knowledgeable guess, an instinctive decision or just common sense based judgment of a problem, scenario or a puzzle. In fact, a heuristic is a most commonly used method for resolving a problem. Heuristics as a noun is an additional name used for heuristic methods. In addition, we can consider heuristic methods as standard control methods for problem solving by human beings as well as machines (Geddes, 2012; Rothlauf, 2011). In addition, heuristics are policies that allow us to cut down the problem solving process and make it more tractable. In view of the fact that there is no guarantee that heuristic methods to resolve a problem will offer the most excellent solution, or even appropriate solution, but they still offer practical useable results (Sooriamurthi, 2011). This paper presents an overview of the modern heuristic techniques. This paper will discuss some of the important aspects that are associated with the modern heuristic methods. Problem Solving Problem solving is a process of resolving a problem. Basically, this process is used to transform the description of the problem into the solution of that problem. In addition, in this process the user makes use of their previous knowledge in order to choose the most appropriate solution for the problem in case there are multiple solutions are available. Additionally, in computer science there are so many ways to resolve a problem. In fact, one solution can be much faster, less expensive and more reliable as compared to others. Hence, it is important to select the best suitable solution for a specific problem. Moreover, in computer science we have a wide variety of tools, strategies, and techniques for resolving a problem. In this scenario, the computers are used as a tool to resolve large and complex problems by creating computer programs using any programming language, which provide the solution of the problem. Some of the wide-ranging techniques for resolving a problem are: program, algorithms and flowchart (Geddes, 2012; Cherry, 2012; Dale et al., 2010). A problem is an obstacle that makes it hard to attain a desired goal, objective or idea. Basically, it refers to a condition, situation or issue that is still unanswered. In many cases, a problem occurs when a person becomes conscious of a major difference between what really is and what is preferred. In fact, the problem solving involves each theoretical problem that requires a response or solution. Attempting to discover a solution to a problem is acknowledged as problem solving. In other words, a problem is a gap between an actual and expected status. For instance, mathematical problem can be an issue regarding mathematical objects and structures that can require a separate answer or clarification or evidence. In this scenario, more examples can include word problems at the school level or more comprehensive problems like that shading a map using just four colors (Geddes, 2012; Cherry, 2012; Dale et al., 2010). Moreover, the entire process of problem solving is based on the mental process as well as is considered to be a fraction of the huge problem that comprises problem identification and problem forming. In addition, it is also recognized as the most difficult among all the intellectual functions. Additionally, the problem solving process starts when a business or corporation or an intelligence system requires a transformation from a known state to a preferred goal state. In view of the fact that the problem is a process which comes under the area of psychology thus, the behavior of human problem solving techniques has been studied by psychologists over the past hundred years. Furthermore, there are numerous techniques for problems solving, such as behaviorism, introspection, computer modeling, simulation and experiments (Geddes, 2012; Cherry, 2012; Dale et al., 2010). Problem Solving Techniques At the present, we have a wide variety of tools and techniques for solving a problem. In this scenario, one of the primary techniques is an abstraction that is aimed at resolving a problem in a model of the system before implementing it to the real system. In addition, we can also use an analogy based problem solving method that makes use of a solution that resolved an analogous (similar) problem. Brainstorming is also a well-known method that is used by a group of people suggesting a number of explanations or ideas and merging them and developing a solution until an optimum is found (Geddes, 2012; Cherry, 2012; Dale et al., 2010). There is another technique known as a divide and conquer method that breaks down a huge, difficult problem into lesser, solvable problems and then resolve those smaller components separately. In addition, the hypothesis testing is the process of supposing a problem, a clarification of the problem and attempting to establish (or, in a number of viewpoints, refute) the assumption. Additionally, the lateral thinking involves assessing solutions ultimately and creatively. On the other hand, Means ends analysis is the process of selecting an action at every step to reach closer to the goal. Morphological analysis is about analyzing the output and communications of the entire system. Here we also have a reduction method that is about transforming a problem into another problem for which a solution exists. Moreover, another most popular method is the research that is about using the existing thoughts or adapting existing ways to resolve related issues. Root cause analysis is about eradicating the reason of the problem. Trial-and-error is about the assessment of testing related solutions until the right one is achieved (Geddes, 2012; Cherry, 2012; Dale et al., 2010). Classical Problem Solving We have a wide variety of classical problem solving techniques that can be used to solve a problem. I have outlined below some of the useful classical problem solving methods: (Forbus, 2008; Geddes, 2012) Attraction methods: this classical problem solving method offers actions of the unidentified “closer together”. Collection methods: This classical problem solving method is used to minimize the extent of the actions of the unknown. Isolation methods: This classical problem solving method is used to minimize the depth of the actions of the unknown. These methods are simple and easy to implement. These methods can be easily managed to get a great deal of support for decision making and problem solving. On the other hand, these methods are time-consuming and also cannot be used to solve huge and complex problems. These issues make the classical problem solving methods problematic for decision making and problem solving (Forbus, 2008; Geddes, 2012; Krantz, 1996). Modern Heuristic Methods (MHM) Modern heuristic methods usually encompass numerous techniques to extract costly information with the intention of directing the search towards the arrangement in a well-organized way. In addition, the modern heuristic methods offer planning graphs that can be simply employed to extract information that help minimize the search space. Though, these heuristics were originally designed for traditional and classical assessment and planning, but they quickly turned out to be functional to most favorable and parallel planning with interesting outcomes (Vlahavas & Dimitris, 2008; Krantz, 1996; Geddes, 2012). The majority of heuristic methods for temporal planning are state-based, where states are typically connected with a time instant (as well recognized as time stamped state). In this scenario, a time stamped state presents an overview of the world and the situation of the planners search at that known time. In addition, it is common to have plans and proceedings connected with them instantly also. Consequently, the fundamental idea of the heuristic is to estimate the sequential distance from a known state to a state that convinces all the objectives of a problem in a graph. Unluckily, constructing a comprehensive temporal planning graph from every state turns out to be very costly, consequently the heuristics are resultant from a tranquil temporal planning graph, anywhere the repose depends at the present on overlooking both the unenthusiastic possessions of the curative events and all the expected temporal constraints (Vlahavas & Dimitris, 2008; Krantz, 1996; Geddes, 2012; Morillo et al., 2003). Modern Heuristic Methods Applications This section discusses some of the possible applications of the modern heuristic methods: Modern Heuristic Methods: Applications in Design of Software For the software development, the application of modern heuristic methods offers a well-designed user interface which allows the system users to instinctively navigate complex systems, lacking complexity. It directs the client when essential with help buttons, tooltips, invites to chat with support, etc., presenting capability to facilitate when required. Though, it is not always a simple process. In fact, software teams, developers and end users similar to others ignore heuristics at their personal peril. In this scenario, the business rules vital to the business are frequently visible to the end-user that they are not expressing to the developer, who can be ignorant with the particular areas of effort the application is destined to serve (Geddes, 2012; Fink & Vob, 1999; Morillo et al., 2003). Modern Heuristic Methods: Applications in Engineering In engineering, a heuristic can be used as an information management process that can be used to resolve process design problems, unreliable from size of equipment to functional circumstances. By using heuristics, the time required to solve a problem can be reduced dramatically. In fact, there are numerous techniques that can be used by the engineers. Some of the well-known techniques include failure mode, results analysis and fault tree analysis. The earlier depends on a group of capable engineers to assess problems, grade them in sequence of significance and then suggest solutions. In addition, the technique of forensic engineering is also a significant source of data and information for examining issues, particularly with the exclusion of improbable reasons and with the weakest link standard. In view of the fact that the heuristics are incomplete, thus it is significant to recognize their boundaries (Geddes, 2012; Fink & Vob, 1999; Morillo et al., 2003). Modern Heuristic Methods applications in LAW The heuristics can be used in the law when a case-by-case examination would be unreasonable, insofar like "expediency" is described through the benefit of a leading body. For example, in a lot of states in the US the legally recognized drinking age is designated as 21years, for the reason that it is disputed that people require to be grown-up sufficient to make choices regarding the problems and risks of alcohol use. In this situation, the somewhat random deadline is used for the reason that it is not possible or not practical to tell whether one person is grown-up sufficient that culture is able to trust them with that type of dependability. It would point youth alcohol strategy further on a case-by-case form and less on a heuristic one, as the results of similar course would seemingly be intentional and not standardized across the people (Geddes, 2012; Fink & Vob, 1999; Morillo et al., 2003). Conclusion Problem solving is the process of solving a problem. There are numerous methods to solve a problem. There are several issues with traditional problem solving techniques. As a result, a new technique of problem solving emerged, which is known as a heuristic. This paper has presented an overview of the heuristic methods and their applications in different areas. A heuristic method is utilized to quickly get a solution that is expected to be close to the possible answer, or optimal answer’. In detail, a heuristic is a rule-of-thumb, a knowledgeable guess, an instinctive decision or just common sense based judgment of a problem, scenario or a puzzle. In fact, a heuristic is a most commonly used method for resolving a problem. Heuristics as a noun is an additional name used for heuristic methods. In addition, we can consider heuristic methods as standard control methods for problem solving by human beings as well as machines. References Cherry, K., 2012. What Is Problem-Solving? [Online] Available at: http://psychology.about.com/od/problemsolving/f/problem-solving-steps.htm [Accessed 28 March 2012]. Dale, N., McMillan, M., Weems, C. & Headington, M., 2010. Chapter 1: Overview of Programming and Problem Solving. [Online] Available at: http://computerscience.jbpub.com/vbNet/pdfs/McMillan01.pdf [Accessed 29 March 2012]. Fink, A. & Vob, S., 1999. Applications of modern heuristic search methods to pattern sequencing problems. Computers and Operations Research, 26(1), pp.17-34. Forbus, K.D., 2008. Classical Problem Solving. [Online] Available at: http://www.cs.northwestern.edu/~forbus/c44/Lectures/Classical%20Problem%20Solving.pdf [Accessed 20 March 2012]. Geddes, J., 2012. Modern Heuristics. [Online] Available at: http://jamesgeddes.com/files/ModernHeuristicsArticles.pdf [Accessed 29 March 2012]. Krantz, S.G., 1996. Techniques of Problem Solving. American Mathematical Society. Morillo, P., Fernandez, M. & Orduna, J.M., 2003. A comparison study of modern heuristics for solving the partitioning problem in distributed virtual environment systems. In ICCSA03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII., 2003. Springer-Verlag Berlin, Heidelberg. Rothlauf, F., 2011. Design of Modern Heuristics: Principles and Application. 1st ed. Springer Publishing Company. Sooriamurthi, R., 2011. 95-864 Heuristic Problem Solving. [Online] Available at: http://www.heinz.cmu.edu/Courses/424syl.pdf [Accessed 29 March 2012]. Vlahavas, I. & Dimitris, V., 2008. Artificial Intelligence for Advanced Problem Solving Techniques. IGI Global. Read More