Return-Path: Received: (majordomo@vger.kernel.org) by vger.kernel.org via listexpand id S261171AbVAHOI4 (ORCPT ); Sat, 8 Jan 2005 09:08:56 -0500 Received: (majordomo@vger.kernel.org) by vger.kernel.org id S261172AbVAHOIz (ORCPT ); Sat, 8 Jan 2005 09:08:55 -0500 Received: from smtp.andrew.cmu.edu ([128.2.10.83]:22687 "EHLO smtp.andrew.cmu.edu") by vger.kernel.org with ESMTP id S261171AbVAHOIy (ORCPT ); Sat, 8 Jan 2005 09:08:54 -0500 Message-ID: <41DFE8B7.9070909@andrew.cmu.edu> Date: Sat, 08 Jan 2005 09:05:43 -0500 From: James Bruce User-Agent: Mozilla Thunderbird 0.9 (X11/20041124) X-Accept-Language: en-us, en MIME-Version: 1.0 To: Jake Moilanen CC: linux-kernel@vger.kernel.org Subject: Re: [ANNOUNCE 0/4][RFC] Genetic Algorithm Library References: <20050106100844.53a762a0@localhost> In-Reply-To: <20050106100844.53a762a0@localhost> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Sender: linux-kernel-owner@vger.kernel.org X-Mailing-List: linux-kernel@vger.kernel.org Content-Length: 1462 Lines: 33 Do you have any crossover? This is critical for GA to work well - without it, the algorithm is really only a parallel random search. More specifically, is step 6 pure copies of a single parents, or can children inherit tunables from multiple parents? - Jim Jake Moilanen wrote: > ... > The basic flow of the genetic algorithm is as follows: > > 1.) Start w/ a broad list of initial tunable values (each set of > tunables is called a child) > 2.) Let each child run for a timeslice. > 3.) Once the timeslice is up, calculate the fitness of the child (how > well performed). > 4.) Run the next child in the list. > 5.) Once all the children have run, compare the fitnesses of each child > and throw away the bottom-half performers. > 6.) Create new children to take the place of the bottom-half performers > using the tunables from the top-half performers. > 7.) Mutate a set number of children to keep variance. > 8.) Goto step 2. > > Over time the tunables should converge toward the optimal settings for > that workload. If the workload changes, the tunables should converge to > the new optimal settings (this is part of the reason for mutation). > This algorithm is used extensively in AI. > ... - To unsubscribe from this list: send the line "unsubscribe linux-kernel" in the body of a message to majordomo@vger.kernel.org More majordomo info at http://vger.kernel.org/majordomo-info.html Please read the FAQ at http://www.tux.org/lkml/