As the design process is supported by many computer-aided tools, computer-aided process planning (CAPP) has evolved to simplify and improve process planning and achieve more effective use of manufacturing resources.
59-540: (Redirected from CAPP ) [REDACTED] Look up capp in Wiktionary, the free dictionary. Capp or CAPP may refer to: In science and technology [ edit ] Computer-aided Process Planning , activities and functions to prepare plans and instructions to manufacture a part or product Computer-aided Production Planning , variant of Computer-aided Process Planning Controlled Access Protection Profile ,
118-400: A bit string . Typically, numeric parameters can be represented by integers , though it is possible to use floating point representations. The floating point representation is natural to evolution strategies and evolutionary programming . The notion of real-valued genetic algorithms has been offered but is really a misnomer because it does not really represent the building block theory that
177-535: A "child" solution using the above methods of crossover and mutation, a new solution is created which typically shares many of the characteristics of its "parents". New parents are selected for each new child, and the process continues until a new population of solutions of appropriate size is generated. Although reproduction methods that are based on the use of two parents are more "biology inspired", some research suggests that more than two "parents" generate higher quality chromosomes. These processes ultimately result in
236-521: A floating point representation. An expansion of the Genetic Algorithm accessible problem domain can be obtained through more complex encoding of the solution pools by concatenating several types of heterogenously encoded genes into one chromosome. This particular approach allows for solving optimization problems that require vastly disparate definition domains for the problem parameters. For instance, in problems of cascaded controller tuning,
295-431: A general rule of thumb genetic algorithms might be useful in problem domains that have a complex fitness landscape as mixing, i.e., mutation in combination with crossover , is designed to move the population away from local optima that a traditional hill climbing algorithm might get stuck in. Observe that commonly used crossover operators cannot change any uniform population. Mutation alone can provide ergodicity of
354-451: A genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes ) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype ) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from
413-473: A less optimal solution. This generational process is repeated until a termination condition has been reached. Common terminating conditions are: Genetic algorithms are simple to implement, but their behavior is difficult to understand. In particular, it is difficult to understand why these algorithms frequently succeed at generating solutions of high fitness when applied to practical problems. The building block hypothesis (BBH) consists of: Goldberg describes
472-427: A linear manner have not been able to satisfy the need for flexible planning, so new dynamic systems will explore all possible combinations of production processes, and then generate plans according to available machining resources. For example, K.S. Lee et al. states that "By considering the multi-selection tasks simultaneously, a specially designed genetic algorithm searches through the entire solution space to identify
531-640: A means to electronically store a process plan once it was created, retrieve it, modify it for a new part and print the plan. Other capabilities were table-driven cost and standard estimating systems, for sales representatives to create customer quotations and estimate delivery time. Generative or dynamic CAPP is the main focus of development, which is the ability to automatically generate production plans for new products, or dynamically update production plans based on resource availability. Generative CAPP will probably use iterative methods, where simple production plans are applied to automatic CAD/CAM development to refine
590-444: A number of steps from maternal DNA adding a number of steps from paternal DNA and so on. This is like adding vectors that more probably may follow a ridge in the phenotypic landscape. Thus, the efficiency of the process may be increased by many orders of magnitude. Moreover, the inversion operator has the opportunity to place steps in consecutive order or any other suitable order in favour of survival or efficiency. A variation, where
649-441: A population of randomly generated individuals, and is an iterative process , with the population in each iteration called a generation . In each generation, the fitness of every individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem being solved. The more fit individuals are stochastically selected from the current population, and each individual's genome
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#1732802217249708-463: A population on each of the computer nodes and migration of individuals among the nodes. Fine-grained parallel genetic algorithms assume an individual on each processor node which acts with neighboring individuals for selection and reproduction. Other variants, like genetic algorithms for online optimization problems, introduce time-dependence or noise in the fitness function. Genetic algorithms with adaptive parameters (adaptive genetic algorithms, AGAs)
767-503: A set of functional and assurance security requirements for information technology products Content Addressable Parallel Processor , type of parallel processor which uses content-addressing memory (CAM) principles Ceramide-activated protein phosphatase , a group of enzymes involved in second-messaging systems Other uses [ edit ] Capp (surname) Andy Capp , British comic strip and fictional character Californians Allied for Patient Protection, coalition to protect
826-421: Is another significant and promising variant of genetic algorithms. The probabilities of crossover (pc) and mutation (pm) greatly determine the degree of solution accuracy and the convergence speed that genetic algorithms can obtain. Researchers have analyzed GA convergence analytically. Instead of using fixed values of pc and pm , AGAs utilize the population information in each generation and adaptively adjust
885-406: Is as an array of bits (also called bit set or bit string ). Arrays of other types and structures can be used in essentially the same way. The main property that makes these genetic representations convenient is that their parts are easily aligned due to their fixed size, which facilitates simple crossover operations. Variable length representations may also be used, but crossover implementation
944-554: Is different from Wikidata All article disambiguation pages All disambiguation pages Computer-aided process planning Process Planning is of two types: Routings that specify operations, operation sequences, work centers, standards, tooling, and fixtures. This routing becomes a major input to the manufacturing resource planning system to define operations for production activity control purposes and define required resources for capacity requirements planning purposes. Computer-aided process planning initially evolved as
1003-495: Is employed. An adequate population size ensures sufficient genetic diversity for the problem at hand, but can lead to a waste of computational resources if set to a value larger than required. In addition to the main operators above, other heuristics may be employed to make the calculation faster or more robust. The speciation heuristic penalizes crossover between candidate solutions that are too similar; this encourages population diversity and helps prevent premature convergence to
1062-441: Is modified ( recombined and possibly randomly mutated) to form a new generation. The new generation of candidate solutions is then used in the next iteration of the algorithm . 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. A typical genetic algorithm requires: A standard representation of each candidate solution
1121-440: Is more complex in this case. Tree-like representations are explored in genetic programming and graph-form representations are explored in evolutionary programming ; a mix of both linear chromosomes and trees is explored in gene expression programming . Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of
1180-460: Is often employed. In this way, small changes in the integer can be readily affected through mutations or crossovers. This has been found to help prevent premature convergence at so-called Hamming walls , in which too many simultaneous mutations (or crossover events) must occur in order to change the chromosome to a better solution. Other approaches involve using arrays of real-valued numbers instead of bit strings to represent chromosomes. Results from
1239-474: Is quite unnatural to model applications in terms of genetic operators like mutation and crossover on bit strings. The pseudobiology adds another level of complexity between you and your problem. Second, genetic algorithms take a very long time on nontrivial problems. [...] [T]he analogy with evolution—where significant progress require [sic] millions of years—can be quite appropriate. [...] I have never encountered any problem where genetic algorithms seemed to me
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#17328022172491298-399: Is the sum of values of all objects in the knapsack if the representation is valid, or 0 otherwise. In some problems, it is hard or even impossible to define the fitness expression; in these cases, a simulation may be used to determine the fitness function value of a phenotype (e.g. computational fluid dynamics is used to determine the air resistance of a vehicle whose shape is encoded as
1357-457: Is worth tuning parameters such as the mutation probability, crossover probability and population size to find reasonable settings for the problem class being worked on. A very small mutation rate may lead to genetic drift (which is non- ergodic in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions, unless elitist selection
1416-806: The Medical Injury Compensation Reform Act of 1975 (MICRA) Canadian Association of Petroleum Producers , voice of the upstream Canadian oil and natural gas industry Canadians Against Proroguing Parliament Canadians Advocating Political Participation Centesimus Annus Pro Pontifice , a foundation established in 1993 Central African Power Pool See also [ edit ] [REDACTED] Search for "capp" on Misplaced Pages. All pages with titles containing Capp or Capps All pages with titles beginning with Capp Capps (disambiguation) CAPPE (disambiguation) CAP (disambiguation) Cap (disambiguation) Topics referred to by
1475-409: The pc and pm in order to maintain the population diversity as well as to sustain the convergence capacity. In AGA (adaptive genetic algorithm), the adjustment of pc and pm depends on the fitness values of the solutions. There are more examples of AGA variants: Successive zooming method is an early example of improving convergence. In CAGA (clustering-based adaptive genetic algorithm), through
1534-417: The 1960s and early 1970s – Rechenberg's group was able to solve complex engineering problems through evolution strategies . Another approach was the evolutionary programming technique of Lawrence J. Fogel , which was proposed for generating artificial intelligence. Evolutionary programming originally used finite state machines for predicting environments, and used variation and selection to optimize
1593-567: The Building Block Hypothesis in adaptively reducing disruptive recombination. Prominent examples of this approach include the mGA, GEMGA and LLGA. Problems which appear to be particularly appropriate for solution by genetic algorithms include timetabling and scheduling problems , and many scheduling software packages are based on GAs . GAs have also been applied to engineering . Genetic algorithms are often applied as an approach to solve global optimization problems. As
1652-743: The LEAD Award in 1995 for this achievement. In order to accomplish Generative CAPP, modifications were made to the CAD, PDM, ERP, and CAM systems. In addition, a Manufacturing Execution System (MES) was built to handle the scheduling of tools, personnel, supply, and logistics, as well as maintain shop floor production capabilities. Generative CAPP systems are built on a factory's production capabilities and capacities. In Discrete Manufacturing, Art-to-Part validations have been performed often, but when considering highly volatile engineering designs, and multiple manufacturing operations with multiple tooling options,
1711-470: The base work already incorporated into them for Generative Computer Aided Process Planning. The task of building and implementing the MES system still requires identifying the capabilities that exist within a given establishment, and exploiting them to the fullest potential. The system created is highly specific, the concepts can be extrapolated to other enterprises. Traditional CAPP methods that optimize plans in
1770-528: The decision tables become longer and the vector matrices more complex. BYJC builds CNC machine tools and Flexible Manufacturing Systems (FMS) to customer specifications. Few are duplicates. The Generative CAPP System is based on the unique capabilities and capacities needed to produce those specific products at BYJC. Unlike a Variant Process Planning system that modifies existing plans, each process plan could be defined automatically, independent of past routings. As improvements are made to production efficiencies,
1829-424: The distribution of the sampling probability tuned to focus in those areas of greater interest. During each successive generation, a portion of the existing population is selected to reproduce for a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions (as measured by a fitness function ) are typically more likely to be selected. Certain selection methods rate
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1888-433: The early 1960s, and the methods were described in books by Fraser and Burnell (1970) and Crosby (1973). Fraser's simulations included all of the essential elements of modern genetic algorithms. In addition, Hans-Joachim Bremermann published a series of papers in the 1960s that also adopted a population of solution to optimization problems, undergoing recombination, mutation, and selection. Bremermann's research also included
1947-454: The elements of modern genetic algorithms. Other noteworthy early pioneers include Richard Friedberg, George Friedman, and Michael Conrad. Many early papers are reprinted by Fogel (1998). Although Barricelli, in work he reported in 1963, had simulated the evolution of ability to play a simple game, artificial evolution only became a widely recognized optimization method as a result of the work of Ingo Rechenberg and Hans-Paul Schwefel in
2006-417: The fitness of each solution and preferentially select the best solutions. Other methods rate only a random sample of the population, as the former process may be very time-consuming. The fitness function is defined over the genetic representation and measures the quality of the represented solution. The fitness function is always problem-dependent. For instance, in the knapsack problem one wants to maximize
2065-479: The general process of constructing a new population is to allow the best organism(s) from the current generation to carry over to the next, unaltered. This strategy is known as elitist selection and guarantees that the solution quality obtained by the GA will not decrease from one generation to the next. Parallel implementations of genetic algorithms come in two flavors. Coarse-grained parallel genetic algorithms assume
2124-453: The generality and/or practicality of the building-block hypothesis as an explanation for GAs' efficiency still remains. Indeed, there is a reasonable amount of work that attempts to understand its limitations from the perspective of estimation of distribution algorithms. The practical use of a genetic algorithm has limitations, especially as compared to alternative optimization algorithms: The simplest algorithm represents each chromosome as
2183-483: The genetic diversity of the subsequent generation of children. Opinion is divided over the importance of crossover versus mutation. There are many references in Fogel (2006) that support the importance of mutation-based search. Although crossover and mutation are known as the main genetic operators, it is possible to use other operators such as regrouping, colonization-extinction, or migration in genetic algorithms. It
2242-442: The heuristic as follows: Despite the lack of consensus regarding the validity of the building-block hypothesis, it has been consistently evaluated and used as reference throughout the years. Many estimation of distribution algorithms , for example, have been proposed in an attempt to provide an environment in which the hypothesis would hold. Although good results have been reported for some classes of problems, skepticism concerning
2301-606: The improvements are automatically incorporated into the current production mix. This generative system is a key component of the CAPP system for the Agile Manufacturing environment. In order to achieve the Generative CAPP system, components were built to meet needed capabilities: The parameters are used to produce multidimensional differential equations. Solving the partial differential equations will produce
2360-680: The initial production plan. A Generative CAPP system was developed at Beijing No. 1 Machine Tool Plant (BYJC) in Beijing, China as part of a UNDP project (DG/CRP/87/027) from 1989 to 1995. The project was reported in "Machine Design Magazine; New Trends" May 9, 1994, P.22-23. The system was demonstrated to the CASA/SME Leadership in Excellence for Applications Development (LEAD) Award committee in July 1995. The committee awarded BYJC
2419-498: The internal loop controller structure can belong to a conventional regulator of three parameters, whereas the external loop could implement a linguistic controller (such as a fuzzy system) which has an inherently different description. This particular form of encoding requires a specialized crossover mechanism that recombines the chromosome by section, and it is a useful tool for the modelling and simulation of complex adaptive systems, especially evolution processes. A practical variant of
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2478-499: The last few mutations to find the absolute optimum. Other techniques (such as simple hill climbing ) are quite efficient at finding absolute optimum in a limited region. Alternating GA and hill climbing can improve the efficiency of GA while overcoming the lack of robustness of hill climbing. This means that the rules of genetic variation may have a different meaning in the natural case. For instance – provided that steps are stored in consecutive order – crossing over may sum
2537-413: The mutation, crossover, inversion and selection operators. The population size depends on the nature of the problem, but typically contains hundreds or thousands of possible solutions. Often, the initial population is generated randomly, allowing the entire range of possible solutions (the search space ). Occasionally, the solutions may be "seeded" in areas where optimal solutions are likely to be found or
2596-427: The next generation population of chromosomes that is different from the initial generation. Generally, the average fitness will have increased by this procedure for the population, since only the best organisms from the first generation are selected for breeding, along with a small proportion of less fit solutions. These less fit solutions ensure genetic diversity within the genetic pool of the parents and therefore ensure
2655-538: The next generation, known as Holland's Schema Theorem . Research in GAs remained largely theoretical until the mid-1980s, when The First International Conference on Genetic Algorithms was held in Pittsburgh, Pennsylvania . In the late 1980s, General Electric started selling the world's first genetic algorithm product, a mainframe-based toolkit designed for industrial processes. In 1989, Axcelis, Inc. released Evolver ,
2714-644: The optimal plan". Genetic algorithm In computer science and operations research , a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators such as selection , crossover , and mutation . Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles , hyperparameter optimization , and causal inference . In
2773-429: The optimum process and production planning at the time when the solution was generated. Solutions had the flexibility to change over time based on the ability to satisfy agile manufacturing criteria. Execution planning can be dynamic and accommodate changing conditions. The system allows new products to be brought on line quickly based on their manufacturability. The more sophisticated CAD/CAM, PDM and ERP systems have
2832-439: The overall genetic algorithm process (seen as a Markov chain ). Examples of problems solved by genetic algorithms include: mirrors designed to funnel sunlight to a solar collector, antennae designed to pick up radio signals in space, walking methods for computer figures, optimal design of aerodynamic bodies in complex flowfields In his Algorithm Design Manual , Skiena advises against genetic algorithms for any task: [I]t
2891-407: The phenotype), or even interactive genetic algorithms are used. The next step is to generate a second generation population of solutions from those selected, through a combination of genetic operators : crossover (also called recombination), and mutation . For each new solution to be produced, a pair of "parent" solutions is selected for breeding from the pool selected previously. By producing
2950-457: The population as a whole is evolved rather than its individual members, is known as gene pool recombination. A number of variations have been developed to attempt to improve performance of GAs on problems with a high degree of fitness epistasis, i.e. where the fitness of a solution consists of interacting subsets of its variables. Such algorithms aim to learn (before exploiting) these beneficial phenotypic interactions. As such, they are aligned with
3009-521: The predictive logics. Genetic algorithms in particular became popular through the work of John Holland in the early 1970s, and particularly his book Adaptation in Natural and Artificial Systems (1975). His work originated with studies of cellular automata , conducted by Holland and his students at the University of Michigan . Holland introduced a formalized framework for predicting the quality of
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#17328022172493068-430: The right way to attack it. Further, I have never seen any computational results reported using genetic algorithms that have favorably impressed me. Stick to simulated annealing for your heuristic search voodoo needs. In 1950, Alan Turing proposed a "learning machine" which would parallel the principles of evolution. Computer simulation of evolution started as early as in 1954 with the work of Nils Aall Barricelli , who
3127-405: The same term [REDACTED] This disambiguation page lists articles associated with the title Capp . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=Capp&oldid=1115084842 " Category : Disambiguation pages Hidden categories: Short description
3186-436: The theory of schemata suggest that in general the smaller the alphabet, the better the performance, but it was initially surprising to researchers that good results were obtained from using real-valued chromosomes. This was explained as the set of real values in a finite population of chromosomes as forming a virtual alphabet (when selection and recombination are dominant) with a much lower cardinality than would be expected from
3245-409: The total value of objects that can be put in a knapsack of some fixed capacity. A representation of a solution might be an array of bits, where each bit represents a different object, and the value of the bit (0 or 1) represents whether or not the object is in the knapsack. Not every such representation is valid, as the size of objects may exceed the capacity of the knapsack. The fitness of the solution
3304-607: The use of clustering analysis to judge the optimization states of the population, the adjustment of pc and pm depends on these optimization states. Recent approaches use more abstract variables for deciding pc and pm . Examples are dominance & co-dominance principles and LIGA (levelized interpolative genetic algorithm), which combines a flexible GA with modified A* search to tackle search space anisotropicity. It can be quite effective to combine GA with other optimization methods. A GA tends to be quite good at finding generally good global solutions, but quite inefficient at finding
3363-596: The world's first commercial GA product for desktop computers. The New York Times technology writer John Markoff wrote about Evolver in 1990, and it remained the only interactive commercial genetic algorithm until 1995. Evolver was sold to Palisade in 1997, translated into several languages, and is currently in its 6th version. Since the 1990s, MATLAB has built in three derivative-free optimization heuristic algorithms (simulated annealing, particle swarm optimization, genetic algorithm) and two direct search algorithms (simplex search, pattern search). Genetic algorithms are
3422-730: Was proposed by John Henry Holland in the 1970s. This theory is not without support though, based on theoretical and experimental results (see below). The basic algorithm performs crossover and mutation at the bit level. Other variants treat the chromosome as a list of numbers which are indexes into an instruction table, nodes in a linked list , hashes , objects , or any other imaginable data structure . Crossover and mutation are performed so as to respect data element boundaries. For most data types, specific variation operators can be designed. Different chromosomal data types seem to work better or worse for different specific problem domains. When bit-string representations of integers are used, Gray coding
3481-603: Was using the computer at the Institute for Advanced Study in Princeton, New Jersey . His 1954 publication was not widely noticed. Starting in 1957, the Australian quantitative geneticist Alex Fraser published a series of papers on simulation of artificial selection of organisms with multiple loci controlling a measurable trait. From these beginnings, computer simulation of evolution by biologists became more common in
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