Distributed evolutionary algorithm for asset management and trading

A networked computer system comprising a server computer, said server computer configured to:

• receive a first gene pool from a first one of a plurality of client computers;
• store received genes in a server gene pool;
• and
• transmit to each of at least a first subset of the plurality of client computers one or more genes of the server gene pool for fitness evaluation spanning W trading days, the first gene pool being generated by evaluating performance characteristic of each of N genes by comparing a solution provided by each gene with the recurrently received data associated with the gene, the performance characteristic of each gene being adjusted with each recurrently received data and defining a fitness of the gene, each gene identifying by a plurality of conditions and at least one action,
• wherein the data associated with each gene comprises historical trading data and wherein the solution provided by each gene comprises a trading recommendation made by the gene,
• wherein said first one of the plurality of client computers is configured to discard M of the N genes after evaluating the fitness of the N genes for P trading days, each of the discarded genes having a fitness that falls below a first predefined threshold value, each of the remaining N-M genes being a surviving gene, wherein M and P are positive integers and wherein M is smaller than N,
• and wherein said first one of the plurality of client computers is further configured to:
• evaluate, for a plurality of Q trading days in addition to the P trading days, the fitness of the remaining genes;
• discard the genes whose fitness as evaluated during a sum of the P+Q trading days is below a second predefined threshold value;
• and
• repeat the evaluation and the discard operations S times, wherein Q and S are integers, and wherein S is equal to or greater than 1, wherein genes surviving S evaluation and discard operations define the first gene pool.

A method for solving a computational problem using a plurality of client computers, the method comprising:

• storing N genes in a memory accessible to a first one of said client computers, each gene characterized by a plurality of conditions and at least one action, wherein N is an integer greater than one;
• through a communications port, recurrently receiving data associated with the N genes;
• said first client computer evaluating performance characteristic of each of the N genes by comparing a solution provided by each gene with the recurrently received data associated with that gene, the performance characteristic of each gene being adjusted with each recurrently received data and defining a fitness of the gene, wherein the data associated with each gene comprises historical trading data and wherein the solution provided by each gene comprises a trading recommendation made by the gene;
• discarding M of the N genes after evaluating the fitness of the N genes for P trading days, each of the discarded genes having a fitness that falls below a first predefined threshold value, each of the remaining N-M genes being a surviving gene, wherein M and P are positive integers and wherein M is smaller than N;
• evaluating, for a plurality of Q trading days in addition to the P trading days, the fitness of the remaining genes;
• discarding genes whose fitness as evaluated during a sum of the P+Q trading days is below a second predefined threshold value;
• repeating the evaluation and the discard operations S times, wherein Q and S are integers, and wherein S is equal to or greater than 1, wherein genes surviving S evaluation and discard operations define a first elitist gene pool;
• and
• transmitting the first elitist pool of genes to a server computer.

A stand alone computer comprising:

• a processing core configured as a server;
• and a plurality of processing cores configured as clients, each of the plurality of clients comprising:
• a memory operative to store N genes, each gene characterized by a plurality of conditions and at least one action, wherein N is an integer greater than one, and
• a port through which the first client processing core recurrently receives data associated with the N genes,
• each client processing core configured to evaluate performance characteristic of each of the N genes by comparing a solution provided by each gene with the recurrently received data associated with that gene, the performance characteristic of each gene being adjusted with each recurrently received data and defining a fitness of the gene, wherein the data associated with each gene comprises historical trading data and wherein the solution provided by each gene comprises a trading recommendation made by the gene,
• wherein a first one of the plurality of client computers is configured to discard M of the N genes after evaluating the fitness of the N genes for P trading days, each of the discarded genes having a fitness that falls below a first predefined threshold value, each of the remaining N-M genes being a surviving gene, wherein M and P are positive integers and wherein M is smaller than N,
• wherein said first one of the client computers is further configured to:
• evaluate, for a plurality of Q trading days in addition to the P trading days, the fitness of the remaining genes;
• discard genes whose fitness as evaluated during a sum of the P+Q trading days is below a second predefined threshold value;
• and
• repeat the evaluation and the discard operations S times, wherein Q and S are integers, and wherein S is equal to or greater than 1, wherein genes surviving S evaluation and discard operations define a first elitist gene pool;
• and
• transmit the first elitist pool of genes to the server processing core.