Evolutionary Algorithms In Ruby

Evolutionary Algorithms in Ruby Julian Fischer fischer@enterprise-rails.de http://www.enterprise-rails.de

Introduction

Introduction About me

About me Julian Fischer ‣Twitter: http://www.twitter.com/railshoster ‣E-Mail: fischer@enterprise-rails.de

About me Julian Fischer ‣Twitter: http://www.twitter.com/railshoster ‣E-Mail: fischer@enterprise-rails.de ‣ CEO of Avarteq GmbH

About me Julian Fischer ‣Twitter: http://www.twitter.com/railshoster ‣E-Mail: fischer@enterprise-rails.de ‣ CEO of Avarteq GmbH ‣ Lecturer „Ruby on Rails“ @ HTWdS

About me Julian Fischer ‣Twitter: http://www.twitter.com/railshoster ‣E-Mail: fischer@enterprise-rails.de ‣ CEO of Avarteq GmbH ‣ Lecturer „Ruby on Rails“ @ HTWdS ‣ Ruby und Ruby on Rails programmer

About me Julian Fischer ‣Twitter: http://www.twitter.com/railshoster ‣E-Mail: fischer@enterprise-rails.de ‣ CEO of Avarteq GmbH ‣ Lecturer „Ruby on Rails“ @ HTWdS ‣ Ruby und Ruby on Rails programmer ‣ Entperise-Rails.de - Head of Hosting

Introduction About Avarteq GmbH

About Avarteq GmbH

About Avarteq GmbH ‣ Founded in Nov. 2008 from two existing companies.

About Avarteq GmbH ‣ Founded in Nov. 2008 from two existing companies. ‣ Involvment of Key-Systems GmbH manages ~2,5 * 10^6 domains for customers of 200+ countries.

About Avarteq GmbH ‣ Founded in Nov. 2008 from two existing companies. ‣ Involvment of Key-Systems GmbH manages ~2,5 * 10^6 domains for customers of 200+ countries. ‣ Team size: 14 people 8 full-time, 6 part-time/freelancer

Introduction Portfolio

About Avarteq GmbH

About Avarteq GmbH ‣ Covers all stages of web development

About Avarteq GmbH ‣ Covers all stages of web development ‣ Consulting

About Avarteq GmbH ‣ Covers all stages of web development ‣ Consulting ‣ Conceptual ~ and screen design

About Avarteq GmbH ‣ Covers all stages of web development ‣ Consulting ‣ Conceptual ~ and screen design ‣ Ruby&onplace. development. In house in Rails

About Avarteq GmbH ‣ Covers all stages of web development ‣ Consulting ‣ Conceptual ~ and screen design ‣ Ruby&onplace. development. In house in Rails ‣ Ruby on Rails hosting/servers/clusters RailsHoster.de - Enterprise-Rails.de

Evolution

Evolution ‣ Population of individuals (organisms)

Evolution ‣ Population of individuals (organisms) ‣ Genes are passed from generation to generation.

Evolution ‣ Population of individuals (organisms) ‣ Genes are passed from generation to generation. ‣ Slightgeneration to thein genetic material from one changes next.

Evolution ‣ Population of individuals (organisms) ‣ Genes are passed from generation to generation. ‣ Slightgeneration to thein genetic material from one changes next. ‣ Differences accumulate over time.

Evolution ‣ Population of individuals (organisms) ‣ Genes are passed from generation to generation. ‣ Slightgeneration to thein genetic material from one changes next. ‣ Differences accumulate over time. ‣ New properties or even species emerge.

Evolution Genetic Operations

Evolution

Evolution ‣ Basic genetic operations

Evolution ‣ Basic genetic operations ‣ Mutation of genetic material. Random changes

Evolution ‣ Basic genetic operations ‣ Mutation of genetic material. Random changes ‣ Recombination of two individuals. Recombine genetic material

Evolution ‣ Basic genetic operations ‣ Mutation of genetic material. Random changes ‣ Recombination of two individuals. Recombine genetic material ‣ also known as sexual reproduction

Evolution

Evolution ‣ Selection

Evolution ‣ Selection ‣ Who will survive and thus

Evolution ‣ Selection ‣ Who will survive and thus ‣ is able to create the most offsprings?

Evolution Challenges

Evolution

Evolution ‣ Changing environment and a lot of other changing temperature, energy resources, competitors and moving stuff, ...

Evolution ‣ Changing environment and a lot of other changing temperature, energy resources, competitors and moving stuff, ... ‣ Huge search space countless possible individuals. vast amount of dna combinations,

Evolution ‣ Changing environment and a lot of other changing temperature, energy resources, competitors and moving stuff, ... ‣ Huge search space countless possible individuals. vast amount of dna combinations, ‣ Restricted resources Death = release of allocated resources.

Evolution

Evolution ‣ Which combinations to create as an individual?

Evolution ‣ Which combinations to create as an individual? ‣ Only validthey usually don‘t breed a banana plant but another If two dogs breed individuals dog, instead!

Evolution ‣ Which combinations to create as an individual? ‣ Only validthey usually don‘t breed a banana plant but another If two dogs breed individuals dog, instead! ‣ Preserve good gene materialetc. have a lot of Maybe that‘s why rockstars, football champions, girlfriends?! Hey, I am a Ruby programmer, that‘s also awesome!

Evolution ‣ Which combinations to create as an individual? ‣ Only validthey usually don‘t breed a banana plant but another If two dogs breed individuals dog, instead! ‣ Preserve good gene materialetc. have a lot of Maybe that‘s why rockstars, football champions, girlfriends?! Hey, I am a Ruby programmer, that‘s also awesome! ‣ Improve! but even improve what‘s good and make it better! Not only preserve

How does that relate to computer science and Ruby?

Evolution and Ruby?

Evolution and Ruby? Given: Search or optimization problem with a huge search space

Evolution and Ruby? Given: Search or optimization problem with a huge search space Wanted: A (heuristic) solution.

Evolutionary Programming (EP) ...

...with Genetic Algorithms (GA)

Genetic Algorithms Basic Idea

Imitate the evolutionary process.

Genetic Algorithms What we need

Genetic Algorithms

Genetic Algorithms ‣ Generation = Nr. of individuals

Genetic Algorithms ‣ Generation = Nr. of individuals ‣ Individualbut a valid problem solution maybe a bad one = valid.

Genetic Algorithms ‣ Generation = Nr. of individuals ‣ Individualbut a valid problem solution maybe a bad one = valid. ‣ Individuals constist of genes

Genetic Algorithms ‣ Generation = Nr. of individuals ‣ Individualbut a valid problem solution maybe a bad one = valid. ‣ Individuals constist of genes ‣ Individuals havethe individual (good .. badfitness indicating the worthiness of a well known solution).

Genetic Algorithms

Genetic Algorithms ‣ Mutation operation

Genetic Algorithms ‣ Mutation operation ‣ Recombination operation

Genetic Algorithms ‣ Mutation operation ‣ Recombination operation ‣ Selection operation

Genetic Algorithms Evolutionary Process

Genetic Algorithms

Genetic Algorithms ‣ Start with a first individual (genesis)

Genetic Algorithms ‣ Start with a first individual (genesis) ‣ Create 2nd individual through mutation

Genetic Algorithms ‣ Start with a first individual (genesis) ‣ Create 2nd individual through mutation ‣ Randomly/Statistically choose a genetic operation to create new individuals

Genetic Algorithms

Genetic Algorithms ‣ If a generation is full create a new one

Genetic Algorithms ‣ If a generation is full create a new one ‣ Select fittest individuals to be passed in to the next generation.

Genetic Algorithms ‣ If a generation is full create a new one ‣ Select fittest individuals to be passed in to the next generation. ‣ Repeat the process until we have an acceptable solution or hit a given boundary.

Concrete?!

Genetic Algorithms Example

Travelling Salesman Problem (TSP)

Traveling Salesman

Traveling Salesman ‣ A salesman needs to travel to n cities

Traveling Salesman ‣ A salesman needs to travel to n cities ‣ Each city needs to be visited once

Traveling Salesman ‣ A salesman needs to travel to n cities ‣ Each city needs to be visited once ‣ The salesman wants to end where he started.

Traveling Salesman ‣ A salesman needs to travel to n cities ‣ Each city needs to be visited once ‣ The salesman wants to end where he started. ‣ The route should be minimal.

Why should we care about salesmen?

Traveling Salesman

Traveling Salesman ‣ TSP can be applied to many scenarios:

Traveling Salesman ‣ TSP can be applied to many scenarios: ‣ (Flight) planning, logistics, ...

Traveling Salesman ‣ TSP can be applied to many scenarios: ‣ (Flight) planning, logistics, ... ‣ Manufactoring of microchips

Traveling Salesman ‣ TSP can be applied to many scenarios: ‣ (Flight) planning, logistics, ... ‣ Manufactoring of microchips ‣ Slightly modified in DNA sequencing

Traveling Salesman ‣ TSP can be applied to many scenarios: ‣ (Flight) planning, logistics, ... ‣ Manufactoring of microchips ‣ Slightly modified in DNA sequencing ‣ ...

Traveling Salesman

Traveling Salesman ‣ What‘s the deal about it?

Traveling Salesman ‣ What‘s the deal about it? ‣ Problem of combinatorical optimization

Traveling Salesman ‣ What‘s the deal about it? ‣ Problem of combinatorical optimization ‣ Not a shortest path problem!

Traveling Salesman ‣ What‘s the deal about it? ‣ Problem of combinatorical optimization ‣ Not a shortest path problem! ‣ Computational complexity: np complete nondeterministic polynomial time

Too many possible combinations!

Traveling Salesman

Traveling Salesman ‣ Most direct solution: Check permutations

Traveling Salesman ‣ Most direct solution: Check permutations ‣ O(n!)

Traveling Salesman ‣ Most direct solution: Check permutations ‣ O(n!) ‣ 5! = 120 combinations

Traveling Salesman ‣ Most direct solution: Check permutations ‣ O(n!) ‣ 5! = 120 combinations ‣ 10! = 3.628.800 combinations

Traveling Salesman ‣ Most direct solution: Check permutations ‣ O(n!) ‣ 5! = 120 combinations ‣ 10! = 3.628.800 combinations ‣ 15! = 1.307.674.368.000 combinations

Traveling Salesman ‣ Most direct solution: Check permutations ‣ O(n!) ‣ 5! = 120 combinations ‣ 10! = 3.628.800 combinations ‣ 15! = 1.307.674.368.000 combinations ‣ 20! ~ 2,43 * 10^18 combinations

Genetic Algorithms TSP

Use a genetic algorithm to find a heuristic solution

TSP Individual

TSP Individual ‣ Valid roundtrip- = cycle of -cities Saarbrücken. Saarbrücken - Amsterdam Madrid - Poznan Berlin -

TSP Individual ‣ Valid roundtrip- = cycle of -cities Saarbrücken. Saarbrücken - Amsterdam Madrid - Poznan Berlin - ‣ Individual = valid roundtrip

TSP Individual ‣ Valid roundtrip- = cycle of -cities Saarbrücken. Saarbrücken - Amsterdam Madrid - Poznan Berlin - ‣ Individual = valid roundtrip ‣ Genes = cities

TSP Fitness

TSP Fitness ‣ Each city knows its geo coords (lat, lon)

TSP Fitness ‣ Each city knows its geo coords (lat, lon) ‣ Calculate distance between two geo coords

TSP Fitness ‣ Each city knows its geo coords (lat, lon) ‣ Calculate distance between two geo coords ‣ Fitness = sum of distances between cities

TSP Genetic Operations

Genetic Operations Mutation

TSP Fitness

TSP Fitness ‣ Shuffle mutation

TSP Fitness ‣ Shuffle mutation ‣ Completely shuffle the order of cities in the roundtrip

TSP Fitness ‣ Shuffle mutation ‣ Completely shuffle the order of cities in the roundtrip ‣ 1-3-5-4-2 → 3-5-1-2-4

TSP Fitness ‣ Shuffle mutation ‣ Completely shuffle the order of cities in the roundtrip ‣ 1-3-5-4-2 → 3-5-1-2-4 ‣ (-) Doesn‘t rely on existing individuals

TSP Fitness ‣ Shuffle mutation ‣ Completely shuffle the order of cities in the roundtrip ‣ 1-3-5-4-2 → 3-5-1-2-4 ‣ (-) Doesn‘t rely on existing individuals ‣ Not a slightly but a massive change

TSP Fitness

TSP Fitness ‣ Partly shuffle

TSP Fitness ‣ Partly shuffle ‣ Exchange two randomly choosen cities

TSP Fitness ‣ Partly shuffle ‣ Exchange two randomly choosen cities ‣ 1-3-5-4-2 → 1-3-4-5-2

TSP Fitness ‣ Partly shuffle ‣ Exchange two randomly choosen cities ‣ 1-3-5-4-2 → 1-3-4-5-2 ‣ (+) Uses existing individuals

TSP Fitness ‣ Partly shuffle ‣ Exchange two randomly choosen cities ‣ 1-3-5-4-2 → 1-3-4-5-2 ‣ (+) Uses existing individuals ‣ Many more mutation operations imaginable.

Genetic Operations Recombination

TSP Recombination

TSP Recombination ‣ Recombination challenge:

TSP Recombination ‣ Recombination challenge: ‣ How to combine parts of individuals in a way that a valid roundtrip will be produced?

Recombination Cycle Crossover

Cycle Crossover

Cycle Crossover ‣ Pass randomly choosen parts from the the parents to the offsprings

Cycle Crossover ‣ Pass randomly choosen parts from the the parents to the offsprings ‣ Creates two offsprings

Cycle Crossover ‣ Pass randomly choosen parts from the the parents to the offsprings ‣ Creates two offsprings ‣ Example

Cycle Crossover ‣ Pass randomly choosen parts from the the parents to the offsprings ‣ Creates two offsprings ‣ Example ‣ mom = [2, 3, 5, 7, 1, 6, 4]

Cycle Crossover ‣ Pass randomly choosen parts from the the parents to the offsprings ‣ Creates two offsprings ‣ Example ‣ mom = [2, 3, 5, 7, 1, 6, 4] ‣ dad = [4, 5, 6, 3, 2, 7, 1]

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1]

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Choose a gene from the first parent (e.g. gene=3, index=1).

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Choose a gene from the first parent (e.g. gene=3, index=1). ‣ Add it to the cycle.

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Choose a gene from the first parent (e.g. gene=3, index=1). ‣ Add it to the cycle. ‣ Cycle: [3]

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for gene with the same index (1) in Dad's genes (gene=5, index=1).

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for gene with the same index (1) in Dad's genes (gene=5, index=1). ‣ Add it to the cycle.

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for gene with the same index (1) in Dad's genes (gene=5, index=1). ‣ Add it to the cycle. ‣ Cycle: [3, 5]

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene (gene=5, index=2) in Mom's genes.

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene (gene=5, index=2) in Mom's genes. ‣ Look for the gene at index 2 at Dad's genes (gene=6, index=2).

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene (gene=5, index=2) in Mom's genes. ‣ Look for the gene at index 2 at Dad's genes (gene=6, index=2). ‣ Add it to the cycle.

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene (gene=5, index=2) in Mom's genes. ‣ Look for the gene at index 2 at Dad's genes (gene=6, index=2). ‣ Add it to the cycle. ‣ Cycle: [3, 5, 6]

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene in Dad's genes (gene=6, index=5).

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene in Dad's genes (gene=6, index=5). ‣ Look for the gene at index 5 (gene=7, index=5) in Mom‘s genes.

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene in Dad's genes (gene=6, index=5). ‣ Look for the gene at index 5 (gene=7, index=5) in Mom‘s genes. ‣ Add it to the cycle.

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene in Dad's genes (gene=6, index=5). ‣ Look for the gene at index 5 (gene=7, index=5) in Mom‘s genes. ‣ Add it to the cycle. ‣ Cycle: [3, 5, 6, 7]

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene in Moms's genes (gene=7, index=3).

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene in Moms's genes (gene=7, index=3). ‣ Look for the gene at index 3 (gene=3, index=3).

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene in Moms's genes (gene=7, index=3). ‣ Look for the gene at index 3 (gene=3, index=3). ‣ Add it to the cycle.

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] ‣ Search for the corresponding gene in Moms's genes (gene=7, index=3). ‣ Look for the gene at index 3 (gene=3, index=3). ‣ Add it to the cycle. ‣ Cycle: [3, 5, 6, 7, 3]

Cycle Crossover cycle = [3, 5, 6, 7, 3]

Cycle Crossover cycle = [3, 5, 6, 7, 3] ‣ Now the cycle is complete!

Cycle Crossover cycle = [3, 5, 6, 7, 3] ‣ Now the cycle is complete! ‣ We can create the offsprings.

Cycle Crossover cycle = [3, 5, 6, 7, 3] ‣ Now the cycle is complete! ‣ We can create the offsprings. ‣ Genes which are in the cycle will be taken from their parents preserving their original positions

Cycle Crossover cycle = [3, 5, 6, 7, 3] ‣ Now the cycle is complete! ‣ We can create the offsprings. ‣ Genes which are in the cycle will be taken from their parents preserving their original positions moms_child = [x, 3, 5, 7, x, 6, x] dads_child = [x, 5, 6, 3, x, 7, x]

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] moms_child = [x, 3, 5, 7, x, 6, x] dads_child = [x, 5, 6, 3, x, 7, x]

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] moms_child = [x, 3, 5, 7, x, 6, x] dads_child = [x, 5, 6, 3, x, 7, x] ‣ Missing genes will be taken from the other parent.

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] moms_child = [x, 3, 5, 7, x, 6, x] dads_child = [x, 5, 6, 3, x, 7, x] ‣ Missing genes will be taken from the other parent. mom = [2, 3, 5, 7, 1, 6, 4] dads_child = [2, 5, 6, 3, 1, 7, 4]

Cycle Crossover mom = [2, 3, 5, 7, 1, 6, 4] dad = [4, 5, 6, 3, 2, 7, 1] moms_child = [x, 3, 5, 7, x, 6, x] dads_child = [x, 5, 6, 3, x, 7, x] ‣ Missing genes will be taken from the other parent. mom = [2, 3, 5, 7, 1, 6, 4] dads_child = [2, 5, 6, 3, 1, 7, 4] dad = [4, 5, 6, 3, 2, 7, 1] moms_child = [4, 3, 5, 7, 2, 6, 1]

TSP Evolver

Setup the evolutionary process

TSP Evolver

TSP Evolver ‣ Generation size

TSP Evolver ‣ Generation size ‣ Choose genetic operations

TSP Evolver ‣ Generation size ‣ Choose genetic operations ‣ Probabilities of genetic operations

TSP Evolver ‣ Generation size ‣ Choose genetic operations ‣ Probabilities of genetic operations ‣ e.g. 30% mutation, 70% x-over

TSP Evolver ‣ Generation size ‣ Choose genetic operations ‣ Probabilities of genetic operations ‣ e.g. 30% mutation, 70% x-over ‣ Set exit condition

TSP Evolver ‣ Generation size ‣ Choose genetic operations ‣ Probabilities of genetic operations ‣ e.g. 30% mutation, 70% x-over ‣ Set exit condition ‣ ...

Code and Demo!

GA Challenges

Genetic Algorithms

Genetic Algorithms ‣ How to represent an individual?

Genetic Algorithms ‣ How to represent an individual? ‣ Find a meaningful fitness function

Genetic Algorithms ‣ How to represent an individual? ‣ Find a meaningful fitness function ‣ Can‘t be applied to boolean fitness functions (match, no match)

Genetic Algorithms ‣ How to represent an individual? ‣ Find a meaningful fitness function ‣ Can‘t be applied to boolean fitness functions (match, no match) ‣ Define than blind guessing skip it! operations If it‘s worse efficient genetic

Genetic Algorithms

Genetic Algorithms ‣ Don‘t get lost in the metaphor! a problem! It‘s not about re-modeling the world, it‘s about solving

Genetic Algorithms ‣ Don‘t get lost in the metaphor! a problem! It‘s not about re-modeling the world, it‘s about solving ‣ Non-determinism 1st run might find a very good solution, 2nd a totally bad one, ...

Genetic Algorithms ‣ Don‘t get lost in the metaphor! a problem! It‘s not about re-modeling the world, it‘s about solving ‣ Non-determinism 1st run might find a very good solution, 2nd a totally bad one, ... ‣ Store your best solutions and use them as the origin for the next run!

What is it good for again?

Genetic Algorithms

Genetic Algorithms ‣ Find good solutions for np hard combinatorical problems

Genetic Algorithms ‣ Find good solutions for np hard combinatorical problems ‣ ... even for difficult search spaces!

What else can be done?

Genetic Algorithms

Genetic Algorithms ‣ Find smart break conditions

Genetic Algorithms ‣ Find smart break conditions ‣ e.g. exit if there is no change for n generations

Genetic Algorithms ‣ Find smart break conditions ‣ e.g. exit if there is no change for n generations ‣ Implement more genetic operations and evaluate them.

Genetic Algorithms

Genetic Algorithms ‣ Optimize ratio between

Genetic Algorithms ‣ Optimize ratio between ‣ Generation size

Genetic Algorithms ‣ Optimize ratio between ‣ Generation size ‣ Operation propabilities

Genetic Algorithms ‣ Optimize ratio between ‣ Generation size ‣ Operation propabilities ‣ Fasten your fitness function!

Genetic Algorithms ‣ Optimize ratio between ‣ Generation size ‣ Operation propabilities ‣ Fasten your fitness function! ‣ Use heuristic fitness functions estimate fitness to be faster

Genetic Algorithms ‣ Optimize ratio between ‣ Generation size ‣ Operation propabilities ‣ Fasten your fitness function! ‣ Use heuristic fitness functions estimate fitness to be faster ‣ Cache frequently computed values e.g. distances between cities

FAQ ‣ Do you use EP/GA for your everyday work?

FAQ ‣ Do you use EP/GA for your everyday work? ‣ Not very often ...

FAQ ‣ Do you use EP/GA for your everyday work? ‣ Not very often ... ‣ ... but how often do you encounter np- hard problems in everyday‘s work?

FAQ ‣ Why talk about it?

FAQ ‣ Why talk about it? ‣ Interesting and generic methodology

FAQ ‣ Why talk about it? ‣ Interesting and generic methodology ‣ Solves NP hard problems heuristically with minimal thought.

FAQ ‣ Why talk about it? ‣ Interesting and generic methodology ‣ Solves NP hard problems heuristically with minimal thought. ‣ Gives a good understanding why some people are what they are: Just bad guesses :-)

Further questions?

Thank you for your attention! Headquarter: Rails Enterprise Hosting: http://www.avarteq.de http://www.enterprise- rails.de Blog: http://ww.treibstofff.de Rails Hosting: http://www.railshoster.de

Resources

Resources ‣ http://en.wikipedia.org/wiki/Travelling_salesman_problem

Resources ‣ http://en.wikipedia.org/wiki/Travelling_salesman_problem ‣ http://en.wikipedia.org/wiki/Evolution

Resources ‣ http://en.wikipedia.org/wiki/Travelling_salesman_problem ‣ http://en.wikipedia.org/wiki/Evolution ‣ http://en.wikipedia.org/wiki/Genetic_algorithm

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Evolutionary Algorithms In Ruby

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