.... calculating success with AI

Genetic Algorithms

A genetic algorithm (GA) is a method for solving optimization problems based on a natural selection process that mimics biological evolution. As such they represent an intelligent exploitation of a random search used to solve optimization problems.

In a genetic algorithm, a population of possible solutions to an optimization problem is evolved toward better solutions. Each solution has a set of properties which can be mutated and altered. Although randomized, GAs are by no means random, instead they exploit historical information to direct the search into the region of better performance within the search space.

The basic process of natural selections in Genetic Algorithms includes:

  • Initialization

  • Evaluation

  • Selection

  • Crossover

  • Mutation

  • Repetition

The basic process in the rp-excel system is the following:

  • An initial database of factors is created (Total of x number of factors).

  • Each factor is then evaluated for its 'fitness'. The fitness value is calculated by how well it fits in with the desired outcomes. These outcomes could be simple, ‘winners only’, or more complex, ‘winners are better but in the money is good as well'. Selection helps by discarding the bad factors and only keeping the best factors in the population. There are a few different selection methods but the basic idea is the same, make it more likely that fitter factors will be selected for the next generation.

  • During crossover new sets of factors are created by combining aspects of the selected factors. The hope is that by combining certain factors an even 'fitter' offspring will be created.

  • Then, mutation typically works by making very small changes at random to individuals factors.

  • Once the next generation of factors is selected, the process is repeated until a termination condition is reached.

Neural Networks

A neural network captures and represents complex input / output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain.

Neural networks resemble the human brain in the following two ways:

  • A neural network acquires knowledge through learning.

  • A neural network's knowledge is stored as weights.

The true power and advantage of neural networks lies in the ability to represent both linear and non-linear relationships. They are also able to learn these relationships directly from the data being modeled. The most common neural network model is the Multilayer Perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output for learning. The goal of this type of network is to create a model that correctly maps the input to the output using historical data. The model can then be used to produce the output when the desired output is unknown.

The difference between a genetic algorithms and neural networks is that a genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems, while neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.

For example, Neural Networks will "find all of the ways a three move sequence in chess will lead to checkmate”, while Genetic Algorithms will "find a sequence of moves with a given set of chess pieces that will lead to checkmate."

Monte Carlo Simulations

Monte Carlo simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making.
The technique is used in widely disparate fields such as

  • finance

  • project management

  • energy

  • manufacturing

  • engineering

  • research and development

  • insurance

  • oil & gas

  • transportation

  • sports forecasting

Monte Carlo simulation presents a range of possible outcomes and the probabilities with which they will occur for any action..
It shows all the possibilities - the outcomes of extremes and the most conservative decisions - as well as all possibilities in between.

The technique was first used by scientists working on the atom bomb; it was named for Monte Carlo, the Monaco resort town renowned for its casinos.
Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems.

©2007  rp-excel.com