Popular recipes tagged "genetic_algorithms"

Artificial Neuroglial Network (ANGN) (Python)

2012-10-02T16:18:36-07:00

Python recipe 578242 by David Adler (artificial_intelligence, genetic_algorithm, genetic_algorithms, neural, neural_networks). Revision 5.

This is an attempt at emulating the algorithm from these scientific articles:

2011 - Artificial Astrocytes Improve Neural Network Performance
2012 - Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

The objective of the program is to train a neural network to classify the four inputs (the dimensions of a flower) into one of three categories (three species of flower), (taken from the Iris Data Set from the UCI Machine Learning Repository). This program has two learning phases: the first is a genetic algorithm (supervised), the second is a neuroglial algorithm (unsupervised). This ANGN is a development of a previous program only consisting of a genetic algorithm which can be found here.

The second phase aims to emulate astrocytic interaction with neurons in the brain. The algorithm is based on two axioms: a) astrocytes are activated by persistent neuronal activity b) astrocytic effects occur over a longer time-scale than neurons. Each neuron has an associated astrocyte which counts the number of times its associated neuron fires (+1 for active -1 for inactive). If the counter reaches its threshold (defined as Athresh) the astrocyte is activated and for the next x iterations (defined as Adur) the astrocyte modifies the incoming weights to that particular neuron. If the counter reached a maximum due to persistent firing the incoming weights are increase by 25% for the proceeding Adur iterations; conversely if the counter reached a minimum due to persistent lack of firing the weights are decreased by 50% for the following Adur iterations). For a detailed description of the algorithm see the linked articles. For a general understanding of how this program was coded look at the pseudo-code/schematic here.

Any comments for improvements are welcome. There are several issues in this program which require addressing, please scroll down below code to read about these issues.

Evolutionary Algorithm (Generation of Prime Numbers) (Python)

2011-11-27T06:45:00-08:00

Python recipe 577964 by Alexander James Wallar (algorithm, example, genetic, genetic_algorithm, genetic_algorithms, list, number, of, prime, primelist, primes, theory).

This is an evolutionary algorithm that returns a random list of prime numbers. This code is highly inefficient for a reason. This algorithm is more of a proof of concept that if a prime was a heritable trait, it would not be a desired one.

Parameters:

isPrime --> n: number to check if it is prime allPrimes --> n: size of list of random primes, m: the primes in the list will be between 0 and m

Partition Problem and natural selection (Python)

2009-10-27T00:27:14-07:00

Python recipe 576937 by Jai Vikram Singh Verma (approximate_solution, complete, crossover, easiest_hard_problem, generations, genetic_algorithms, natural_selection, np, np_complete, np_hard, optimal_solution, partition_problem). Revision 4.

Partition problem From Wikipedia, the free encyclopedia

In computer science, the partition problem is an NP-complete problem. The problem is to decide whether a given multiset of integers can be partitioned into two "halves" that have the same sum. More precisely, given a multiset S of integers, is there a way to partition S into two subsets S1 and S2 such that the sum of the numbers in S1 equals the sum of the numbers in S2? The subsets S1 and S2 must form a partition in the sense that they are disjoint and they cover S. The optimization version asks for the "best" partition, and can be stated as: Find a partition into two subsets S1,S2 such that max(sum(S_1), sum(S_2)) is minimized (sometimes with the additional constraint that the sizes of the two sets in the partition must be equal, or differ by at most 1).

The partition problem is equivalent to the following special case of the subset sum problem: given a set S of integers, is there a subset S1 of S that sums to exactly t /2 where t is the sum of all elements of S? (The equivalence can be seen by defining S2 to be the difference S − S1.) Therefore, the pseudo-polynomial time dynamic programming solution to subset sum applies to the partition problem as well.

Although the partition problem is NP-complete, there are heuristics that solve the problem in many instances, either optimally or approximately. For this reason, it has been called the "The Easiest Hard Problem" by Brian Hayes.