Mathematical Software - Machine Learning - OptFinderML

Software for computer simulations of learning systems. Machine Learning software is aimed at finding such a system configuration that allows to cooperate with environment most efficiently. To learn more about machine learning go to machine learning page.
The OptFinderML software is written in Java and can be run on various operating systems. This numerical package is designed for Java(TM) SE Runtime Environment version 8.
Category: Mathematical Software
Brand: OptfinderML
Created by: Ilona Dominika Kosińska

OPTFINDERML 1.1 RELEASE

The table below presents available program options.
At the very beginning, an user should design a set of rules. Each rule contains a pair of condition and corresponding system answer when this condition is satisfied. Our system cooperates with an outer structure called environment or surroundings. This collaboration is done by a flow of information between the both structures. Firstly, environment send a set of messages to our system. They can be loaded from a file or downloaded from a remote server by an internet connection. Then on the basis of assumed rules system generates its answers. If an obtained message matches a condition pattern the message is allowed to attend an auction. The user must decide which auction schemes is going to use. Available options are listed in the table below. When the auction is done then the generated answer is sent back to environment. This can be done either by writing data to a file or by uploading data via internet. It depends on user's requirements. Consequently, these system answers are detected and interpreted the an outer structure. When answers satisfy our outer 'teacher' then system is granted otherwise is punished. This exchange of information teaches our system how to react correctly to messages generated by environment.
During OptfinderMl simulations conditions are treated as genotypes in genetic algorithms. To each condition (genotype) is assigned a numeric value being its strength. The genotype's strength (=classifier's strength) is equivalent to the fitness function in genetic algorithms. Thus, all parameters used in genetic algorithms find their applications also in the OptfinderML simulation. All of them are presented in the table below.

machine learning, genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease machine learning, genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
TYPE
classifier system machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
MESSAGES (SIGNALS)
loaded from file machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
RULES
manually set by an user machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
random machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
loaded from file machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
AUCTION
one step routine machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
cascade routine machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
SYSTEM OUTPUT
written to file machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
ENVIRONMENTAL FEEDBACK
loaded from file machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
GENOTYPE
haploid machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
diploid machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
ENCODING METHOD
ternary digits: {0, 1, #} machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
other machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
STRENGTH AS FITNESS FUNCTION WITH
linear scaling machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
power scaling machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
σ - truncation machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
SELECTION METHODS
roulette method machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
modified roulette method machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
deterministic machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
random choice according to rest parts (type I, roulette method) machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
random choice according to rest parts (type II, Bernoullie's method) machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
tournament method machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
DIPLOIDY WITH DOMINANCE
triallelic-model (Hollstien-Holland) machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
SHARING FUNCTION COMPUTED
at the strength level machine learning,genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
at the genotype level genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
as a power function genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
DE JONG MODELS
from R1 to R6 genetic algorithms, genetic populations, computer simulations, optfinderML, taketechease
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