What Types of Problems Do Genetic Algorithms Solve? (Professional Only)

A genetic algorithm is a computational method modeled on biological evolutionary processes that can be used to find the optimum solution to a problem that may have many solutions. For example, you can use genetic algorithms to find the maximum and the minimum of a function, to find an optimal route on a map, or to minimize the cost of manufacturing your products. They can be used to find an optimal set of parameters (such as the optimal number of shares of each type of stock in a portfolio), or to select an optimal sequence of events (such as the shortest route to cover a sales territory). In other words, genetic algorithms can be powerful tools for finding the best of millions of possible solutions for different real life problems.

These algorithms have been found to be very powerful in solving optimization problems that appear to be difficult or even unsolvable by traditional methods. Genetic algorithms use a minimum of information about the problem and they only require a quantitative estimation of the quality of a possible solution. This makes them easy to use and applicable to most optimization problems.

Genetic Algorithm Applications

The following is a list of some of the applications developed by Ward Systems Group customers. Business applications for genetic algorithms include:
Optimizing process control operations
Scheduling salesman or delivery routes
Minimizing manufacturing time
Optimizing budget allocations to different departments to maximize profit
Optimizing retail site locations
Group packages with different dimensions into delivery lots
Generating rules to predict a rise in stock indexes
Optimally group stocks with different values into balanced portfolios
Minimize financial risk managing a fund while simultaneously trying to maximize return
Optimize technical indicators
Finding optimal trading strategies
Optimize treatment programs
Schedule use of medical treatment practitioners and/or facilities
Classify patients into groups for experimental studies
Schedule medical students through training and clinical rotations
Maximize polymer qualities based upon different composition materials
Optimize chemical reactions based on different chemicals or processing times
Maximize forest yield by varying types of trees planted, fertilization, and cutting practices

And many more!


Topics of Interest:

What are Genetic Algorithms?
How Does a Genetic Algorithm Work?
Why is a Genetic Algorithm Better Than Other Optimizers?
How are Genetic Algorithms Used in the NeuroShell Trader Professional?

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