Randy Olson Posted in analysisdata visualizationmachine learning Last week, Tracy Staedter from Discovery News proposed an interesting idea to me: Planning the road trip One of the hardest parts of planning a road trip is deciding where to stop along the way.
Every tree node has an operator function and every terminal node has an operand, making mathematical expressions easy to evolve and evaluate. Thus traditionally GP favors the use of programming languages that naturally embody tree structures for example, Lisp ; other functional programming languages are also suitable.
Non-tree representations have been suggested and successfully implemented, such as linear genetic programming which suits the more traditional imperative languages [see, for example, Banzhaf et al.
Other program representations on which significant research and development have been conducted include programs for stack-based virtual machines   and sequences of integers that are mapped to arbitrary programming languages via grammars  .
Cartesian genetic programming is another form of GP, which uses a graph representation instead of the usual tree based representation to encode computer programs. Most representations have structurally noneffective code introns.
Such non-coding genes may seem to be useless, because they have no effect on the performance of any one individual. However, they alter the probabilities of generating different offspring under the variation operators, and thus alter the individual's variational properties.
Experiments seem to show faster convergence when using program representations that allow such non-coding genes, compared to program representations that do not have any non-coding genes.
The individuals are selected probabilistically such that the better performing individuals have a higher chance of getting selected . The most commonly used selection method in GP is Tournament selectionalthough other methods such as Fitness proportionate selectionLexicase Selection and many others have been demonstrated to perform better for many GP problems.
Variation[ edit ] Various genetic operators i. The rate at which these operators are applied determine the diversity in the population. Applications[ edit ] GP has been successfully used as an automatic programming tool, a machine learning tool and an automatic problem-solving engine .
GP is especially useful in the domains where the exact form of the solution is not known in advance or an approximates solution is acceptable possibly because finding the exact solution is very difficult. Some of the applications of GP are curve fitting, data modeling, Symbolic regressionfeature selection, classification, etc.
Koza mentions 76 instances where Genetic Programming has been able to produce results that are competitive with human-produced results called Human-competitive results . Sincethe annual Genetic and Evolutionary Computation Conference GECCO holds Human Competitive Awards called Humies competition where cash awards are presented to human-competitive results produced by any form of genetic and evolutionary computation.
GP has won many awards in this competition over the years.
Meta-genetic programming[ edit ] Meta-genetic programming is the proposed meta learning technique of evolving a genetic programming system using genetic programming itself. It suggests that chromosomes, crossover, and mutation were themselves evolved, therefore like their real life counterparts should be allowed to change on their own rather than being determined by a human programmer.
Doug Lenat 's Eurisko is an earlier effort that may be the same technique. It is a recursive but terminating algorithm, allowing it to avoid infinite recursion.
In the "autoconstructive evolution" approach to meta-genetic programming, the methods for the production and variation of offspring are encoded within the evolving programs themselves, and programs are executed to produce new programs to be added to the population.
However, it might be possible to constrain the fitness criterion onto a general class of results, and so obtain an evolved GP that would more efficiently produce results for sub-classes. This might take the form of a meta evolved GP for producing human walking algorithms which is then used to evolve human running, jumping, etc.
The fitness criterion applied to the meta GP would simply be one of efficiency.Last week, Tracy Staedter from Discovery News proposed an interesting idea to me: Why not use the same algorithm from my Where’s Waldo article to compute the optimal road trip across every state in the U.S.?
Visiting every U.S. state has long been on my bucket list, so I jumped on the opportunity and opened up my machine learning tool box for another quick weekend project.
Neural network software is used to simulate, research, develop and apply artificial/biological neural networks. It can be turned in to a powerful and reliable neural network based forecasting tool for stock market, sales forecast, investment tools and optimization application.
Genetic programming is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, optimal control, classification, system identification, function learning, and computational molecular biology.
The Simple Genetic Algorithm (SGA) is a classical form of genetic search.
Viewing the SGA as a mathematical object, Michael D. Vose provides an introduction to what is known (i.e., proven) about the theory of the SGA.
Family Tree DNA: Genetic Testing Service Get genetically tested to discover your relationship to other families, other Jews, and other ethnic groups. Last week, Tracy Staedter from Discovery News proposed an interesting idea to me: Why not use the same algorithm from my Where’s Waldo article to compute the optimal road trip across every state in the U.S.?
Visiting every U.S. state has long been on my bucket list, so I jumped on the opportunity and opened up my machine learning tool box for .