Industrial application

industrial

1. Industrial application By B.Kohila M.Sc(IT)

2. content Genetic algorithm Introduction Software package Data structure Fitness assignment Selection function Mutation operators Automotive design Engineering design Robotics Optimized telecommunications routing Evolvable hardware Computer gaming

3. Genetic algorithm Genetic Algorithm (GA) is a calculus free optimization technique based on principles of natural selection for reproduction and various evolutionary operations such as crossover, and mutation. Various steps involved in carrying out optimization through GA are described. Three applications, viz. finding maximum of a mathematical function, obtaining estimates for a multiple linear regression model, and fitting a nonlinear statistical model through GA procedure, are discussed. Finally, results are compared to those obtained from standard (calculus based) solution techniques .

4. Introduction A genetic algorithm (GA) is a search and optimization method which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary strings. Every solution is assigned a fitness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modified to a new population by applying three operators similar to natural genetic operators reproduction , crossover, and mutation. It works iteratively by successively applying these three operators in each generation till a termination criterion is satisfied. Over the past decade and more, GAs have been successfully applied to a wide variety of problems, because of their simplicity, global perspective, and inherent parallel processing.

5. Software Packages Whilst there exist many good public-domain genetic algorithm packages, such as GENESYS and GENITOR, none of these provide an environment that is immediately compatible with existing tools in the control domain. The MATLAB Genetic Algorithm Toolbox aims to make GAs easily accessible. This allows the retention of existing modeling and simulation tools for building objective functions and allows the user to make direct comparisons between genetic methods and traditional procedures.

6. Data Structures The main data structures in the GA Toolbox are chromosomes, phenotypes, objective function values and fitness values. The chromosome structure stores an entire population in a single matrix of size Nind × Lind, where Nind is the number of individuals and Lind is the length of the chromosome structure. Phenotypes are stored in a matrix of dimensions Nind × Nvar where Nvar is the number of decision variables. An Nind × Nobj matrix stores the objective function values, where Nobj is the number of objectives . Finally, the fitness values are stored in a vector of length Mind. In all of these data structures, each row corresponds to a particular individual.

7. Fitness Assignment: The fitness function transforms the raw objective function values into non-negative figures of merit for each individual. The Toolbox supports the offsetting and scaling method and the linear-ranking algorithm. In addition, non-linear ranking is also supported in the routine ranking .

8. Selection Functions These functions select a given number of individuals from the current population, according to their fitness, and return a column vector to their indices. Currently available routines are roulette wheel selection, rows, and stochastic universal sampling, sues. A high-level entry function, select, is also provided as a convenient interface to the selection routines, particularly where multiple populations are used. In cases where a generation gap is required, i.e. where the entire population is not reproduced in each generation, reins can be used to effect uniform random or fitness-based re-insertion .

9. Mutation Operators Binary and integer mutation are performed by the routine mutt. Real-value mutation is available using the breeder GA mutation function, mudbug. Again, a high-level entry function, mutate, to the mutation operators is provided .

10. Automotive Design Using Genetic Algorithms [GAs] to both design composite materials and aerodynamic shapes for  and regular means of transportation (including aviation) can return combinations of best materials and best engineering to provide faster, lighter, more fuel efficient and safer vehicles for all the things we use vehicles for. Rather than spending years in laboratories working with polymers, wind tunnels and balsa wood shapes, the processes can be done much quicker and more efficiently by computer modeling using GA searches to return a range of options human designers can then put together however they please .

11. Engineering Design Getting the most out of a range of materials to optimize the structural and operational design of buildings, factories, machines, etc. is a rapidly expanding application of GAs. These are being created for such uses as optimizing the design of heat exchangers, robot gripping arms, satellite booms, building trusses, flywheels, turbines, and just about any other computer-assisted engineering design application. There is work to combine GAs optimizing particular aspects of engineering problems to work together, and some of these can not only solve design problems, but also project them forward to analyze weaknesses and possible point failures in the future so these can be avoided.

12.  Robotics Robotics involves human designers and engineers trying out all sorts of things in order to create useful machines that can do work for humans. Each robot’s design is dependent on the job or jobs it is intended to do, so there are many different designs out there. GAs can be programmed to search for a range of optimal designs and components for each specific use, or to return results for entirely new types of robots that can perform multiple tasks and have more general application. GA-designed robotics just might get us those nifty multi-purpose, learning robots we’ve been expecting any year now since we watched the Jet sons as kids, who will cook our meals, do our laundry and even clean the bathroom for us!

13. Optimized Telecommunications Routing Do you find yourself frustrated by slow LAN performance, inconsistent internet access, a FAX machine that only sends faxes sometimes, your land line’s number of ‘ghost’ phone calls every month? Well, GAs are being developed that will allow for dynamic and anticipatory routing of circuits for  These could take notice of your system’s instability and anticipate your re-routing needs. Using more than one GA circuit-search at a time, soon your interpersonal communications problems may really be all in your head rather than in your telecommunications system. Other GAs are being developed to optimize placement and routing of cell towers for best coverage and ease of switching, so your cell phone and blackberry will be thankful for GAs too.

14. Evolvable Hardware  applications are electronic circuits created by GA computer models that use stochastic (statistically random) operators to evolve new configurations from old ones. As the algorithm does its thing in the running model, eventually a circuit configuration will come along that does what the designer wants. Think of reconfigurable circuits in something like a space robot. It could use a built-in GA library and simulator to re-design itself after something like radiation exposure that messes up its normal configuration, or encounters a novel situation in which it needs a function it doesn’t already have. Such GAs would enable self-adaptation and self-repair

15.  Computer Gaming Those who spend some of their time playing computer Sims games (creating their own civilizations and evolving them) will often find themselves playing against  sophisticated artificial intelligence GAs  instead of against other human players online. These GAs have been programmed to incorporate the most successful strategies from previous games – the programs ‘learn’ – and usually incorporate data derived from game theory in their design.  Game theory  is useful in most all GA applications for seeking solutions to whatever problems they are applied to, even if the application really is a game.

16. Thank you