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CS631 – Research Trends in Artificial Intelligence
Ayesha Farrukh, 2004-03-0004
Junaid Akhtar, 2004-03-0019
Applications of Genetic Algorithms
Genetic Algorithms (GAs) fall under the heading of Evolutionary Algorithms (EAs). EAs are used to solve problems
that do not already have a well defined efficient solution, and the problem domain is NP-complete. [5] GAs have
been used to solve optimization problems (scheduling, shortest path, etc), and in modelling systems where
randomness is involved (e.g., the stock market). Several applications of GAs follow.
Genetic Programming
Genetic programming is a way of evolving algorithms that will map data to a given result when no set formula is
known. Mathematicians/programmers can easily find algorithms to solve problems that deal with 5 or so variables,
but when the unknowns increase to 10, 20, 50 or more variables, the problem becomes close to impossible to solve.
In a case where the mathematical data is available and answers are available but the expression that joins the data to
the answers is missing, a genetic algorithm can be used to 'evolve' an expression tree to create a very close fit to the
data. [4] A GP-powered program works by generating a series of randomly generated expression trees that represent
various formulas. These trees are then tested against the data, poor ones discarded, good ones kept and bred.
Mutation, crossover, and all of the elements in genetic algorithms are used to breed the 'highest-fitness' tree for the
given problem. At best, this will perfectly match the variables to the answer, other times it will generate an answer
very close to the desired answer. [1]
Evolvable Systems
Computer scientists at NASA are building software programs that design hardware using evolutionary algorithms.
They have developed a high-tech communications antenna capable of sending and receiving signals while orbiting
Earth. The antenna is smaller than a half-dollar coin. The design is extremely different from conventional human
made antennas.
Their genetic algorithm starts with a set of human-made specifications for antennas from which the
program will generate populations of hundreds of designs, each encoded in an artificial chromosome. For an
antenna, genes might specify its branching structure and the lengths and widths of each wire. The program's first
populations are usually quite rough, varying among themselves in their makeup, but will produce superior designs
by repeatedly taking the best antennas and using them as "parents" to make new ones. [10]
Composing Music
GAs have been used to ‘compose’ music of different genres and also to ‘listen’ to music and judge its quality. One
such application is ‘GenJam’, short for Genetic Jammer. It is an interactive genetic algorithm (IGA) that models a
jazz improviser and performs as a featured soloist in the author’s Virtual Quintet. GenJam evolves populations of
melodic ideas under the guidance of a human mentor, whose feedback provides the environment under which
individual ideas either survive to breed or die off. GenJam also uses its genetic algorithm machinery as a real-time
melodic development paradigm to evolve phrases played by a human into its improvised responses in chase
choruses. [2]
Gaming
Genetic Algorithms are excellent at searching very large problem spaces, and also for evolutionary development.
For example, a large structure of possible traits for Quake II monsters (aggressiveness, probability of running away
when low on health, probability of running away when few in numbers etc) can be created and then a genetic
algorithm can be used to find the best combination of these structures to beat the player. A player would go through
a level of the game and at the end, the program would pick the monsters that fared the best against the player, and
use those in the next generation. Slowly, after a lot of playing, some reasonable characteristics would be evolved. [6]
Market Strategies
GAs are often used to model stock markets or markets in general to solve optimization problems where the objective
is to maximize profit or predict market behaviour. One specific application has been portfolio selection. Portfolio
theory considers a sequential portfolio selection procedure for investing in some investment media such as stock
market, etc. with the goal of best analyzing and predicting the performance of the future market. The performance of
a portfolio theory model is directly linked to the portfolio prediction. In the model, the quantifiable microeconomic
stock data are first optimized through GAs to generate the most effective microeconomic data in relation to the stock
market performance. Neural networks and fuzzy logic are used to make further decisions. [8]
CS631 – Research Trends in Artificial Intelligence
Ayesha Farrukh, 2004-03-0004
Junaid Akhtar, 2004-03-0019
Another application is predicting the profits of each player in a market given the marketing actions of all
players. The project reported here [7] is concerned with trying to explain and to improve upon the historical
behaviour and profits of a group of sellers, as recorded in supermarket scanner data. GAs have been used to derive
mappings which may explain the behaviour of brand managers in an oligopolistic retail market for coffee, and,
second, to attempt to improve on the historical profits of these brand managers, pitted in weekly competition with
each other, vying for sales and profits with their different brands of ground, sealed coffee on the supermarket
shelves.
Robotics
Evolutionary robotics is a new technique for the automatic creation of autonomous robots. Inspired by the
Darwinian principle of selective reproduction of the fittest, it views robots as autonomous artificial organisms that
develop their own skills in close interaction with the environment and without human intervention. Drawing heavily
on biology and ethology, it uses the tools of neural networks, genetic algorithms, dynamic systems, and biomorphic
engineering. The resulting robots share with simple biological systems the characteristics of robustness, simplicity,
small size, flexibility, and modularity. [3]
In evolutionary robotics, an initial population of artificial chromosomes, each encoding the control system
of a robot, is randomly created and put into the environment. Each robot is then free to act (move, look around,
manipulate) according to its genetically specified controller while its performance on various tasks is automatically
evaluated. The fittest robots then "reproduce" by swapping parts of their genetic material with small random
mutations. The process is repeated until the "birth" of a robot that satisfies the performance criteria. [3]
Neural Networks
Genetic algorithms can be combined with neural networks to enhance their performance by taking some of the
guesswork out of optimally choosing neural network parameters. In general, genetic algorithms can be used in
conjunction with neural networks in the following four ways. They can be used to choose the best inputs to the
neural network, optimize the neural network parameters (such as the learning rates, number of hidden layer
processing elements, etc.), train the actual network weights (rather than using back-propagation), or to
choose/modify the neural network architecture. In each of these cases, the neural network error is the fitness used to
rank the potential solutions. [9]
Industrial Optimization
An application for ore grinding has brought real-time expert systems, on-line adaptive modeling, and optimization
together at the Los Pelambres copper minerals concentrator in central Chile. The objective was to operate the
grinding mill at the highest possible feed rate no matter how hard or soft the ore. To do so, an expert control system
based on genetic algorithms was employed to emulate the functions of the expert plant operators. These features
allowed the controller to evaluate alternative operating conditions for future effectiveness, and then implement the
best set, provided it was going to outperform the current control algorithm. Decisions based on this system have
proven to be so accurate that over 50% of the mill's regulatory set points are now being generated by the GA based
system and production has increased by 8%. [11]
References
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[8]
[9]
[10]
[11]
“Military Applications of AI” http://library.thinkquest.org/18242/app_military.shtml
Biles, J.A., “Life with GenJam: interacting with a musical IGA”, IEEE SMC '99 Conference Proceedings on Systems, Man, and
Cybernetics, 12-15 Oct. 1999 vol. 3, pp 652 - 656
Stefano Nolfi, Dario Floreano. “Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines”
http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=3684
“Introduction to Genetic Algorithms”, http://library.thinkquest.org/18242/ga.shtml
“What Applications of EAs are there?” http://www-2.cs.cmu.edu/Groups/AI/html/faqs/ai/genetic/part3/faq-doc-1.html
“AI in Gaming” http://library.thinkquest.org/18242/app_game.shtml
Shiraz, G.M., Marks, R.E., Midgley, D.F., Cooper, L.G., “Using genetic algorithms to breed competitive marketing strategies”, IEEE
International Conference on Systems, Man, and Cybernetics, 11-14 Oct. 1998
pp 2367 - 2372 vol. 3
Paul Bao, Hakman Wong; “A hybrid portfolio theory model based on genetic algorithm and vector quantization”, IEEE International
Conference on Systems, Man, and Cybernetics, 1998, 11-14 Oct. 1998
pp 4301 - 4306 vol.5
“NeuroSolutions” http://www.neurosolutions.com/products/ns/faq.html
J.D. Lohn, G. S. Hornby, D. S. Linden, “An Evolved Antenna for Development On NASA’s Space Technology 5 Mission”
“Real-World Applications of Advanced Process Controls” http://www.manufacturing.net/ctl/article/CA154932