diff options
Diffstat (limited to 'watchmaker/book/src/xml/islands.xml')
| -rw-r--r-- | watchmaker/book/src/xml/islands.xml | 175 |
1 files changed, 175 insertions, 0 deletions
diff --git a/watchmaker/book/src/xml/islands.xml b/watchmaker/book/src/xml/islands.xml new file mode 100644 index 0000000..e7943f8 --- /dev/null +++ b/watchmaker/book/src/xml/islands.xml @@ -0,0 +1,175 @@ +<chapter xmlns="http://docbook.org/ns/docbook" + xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" + xsi:schemaLocation="http://docbook.org/ns/docbook http://www.docbook.org/xml/5.0/xsd/docbook.xsd"> + <title>Island Models</title> + <indexterm><primary>island models</primary></indexterm> + <indexterm><primary>Australia</primary></indexterm> + <para> + In the natural world, populations of organisms might be separated by geography. Left to evolve in isolation + over millions of years, vastly different species will occur in different locations. Consider Australia, + an island continent protected by its seas. With little opportunity for outside organisms to + interfere, and few opportunities for its land-based organisms to migrate to other land masses, Australian + wildlife evolved to be distinctly different from that of other continents and countries. The majority of + Australia's plant and animal species, including 84% of its mammals, are endemic. They occur nowhere else + in the world. + </para> + <indexterm><primary>Darwin, Charles</primary></indexterm> + <indexterm><primary>Galápagos Islands</primary></indexterm> + <para> + Australia is not the only island to exhibit such levels of endemism. It was a visit to the Galápagos + Islands in 1835 that started Charles Darwin on the path to formulating his theory of evolution. Darwin + noticed the pronounced differences between the species of mocking birds and tortoises present on the + different islands of the archipelago and began to speculate on how such variations might have occurred. + </para> + <para> + In the world of evolutionary computation we can mimic this idea of having multiple isolated populations + evolving in parallel. Having additional populations would increase the likelihood of finding a solution that + is close to the global optimum. However, it is not just a question of having a larger global population. + A system of 10 islands each with a population of 50 individuals is not equivalent to a single island with a + population of 500. The reason for this is that the island system partitions the search. If one island + prematurely converges on a sub-optimal solution it does not affect the evolution happening on the other + islands; they are following their own paths. A single large population does not have this in-built + resilience. + </para> + <section> + <title>Migration</title> + <indexterm><primary>migration</primary></indexterm> + <indexterm><primary>island models</primary><secondary>migration</secondary></indexterm> + <para> + There is of course no real difference between evolving 10 completely separate islands in parallel and running + the same single-population evolution 10 times in a row, other than how the computing resources are utilised. + In practice the populations are not kept permanently isolated from each other and there are occasional + opportunities for individuals to migrate between islands. + </para> + <para> + In nature external species have been introduced to foreign ecosystems in several ways. In an ice age the waters + that previously separated two land masses might freeze providing a route for land animals to migrate to + previously unreachable places. Microorganisms and insects have often strayed beyond their usual environment by + hitching a ride with larger species. + </para> + <indexterm><primary>rabbits</primary></indexterm> + <indexterm><primary>Austin, Thomas</primary></indexterm> + <para> + The effect of introducing a foreign species to a new environment can vary. The new species might be + ill-adapted to its new surroundings and quickly perish. Alternatively, a lack of natural predators + may cause it to flourish, often to the detriment of indigenous species. One such example is the + introduction of rabbits to Australia. Australia was a land without rabbits until the arrival of European + settlers. An Englishman named Thomas Austin released 24 rabbits into the wild of Victoria in October 1859 + with the intention of hunting them. If rabbits are famous for one thing it is for reproducing prodigiously. + The mild winters allowed year-round breeding and the absence of any natural rabbit predators, such as foxes, + allowed the Australian rabbit population to explode unchecked. Within 10 years an annual cull of two million + rabbits was having no noticeable effect on rabbit numbers and the habitats of some native animals were being + destroyed by the floppy-eared pests. Today there are hundreds of millions of rabbits in Australia, despite + efforts to reduce the population, and the name of Thomas Austin is widely cursed for his catastrophic lack + of foresight. + </para> + <para> + While such invasions of separate species provide a useful analogy for what can happen when we introduce migration + into island model evolutionary algorithms, we are specifically interested in the effects of migration involving + genetically different members of the same species. This is because, in our simplified model of evolution, + all individuals are compatible and can reproduce. The island model of evolution provides the isolation necessary + for diversity to thrive while still providing opportunities for diverse individuals to be combined to produce + potentially fitter offspring. + </para> + <para> + In an island model, the isolation of the separate populations often leads to different traits originating on + different islands. Migration brings these diverse individuals together occasionally to see what happens when + they are combined. Remember that, even if the immigrants are weak, cross-over can result in offspring that are + fitter than either of their parents. In this way, the introduction to the population of new genetic building + blocks may result in evolutionary progress even if the immigrants themselves are not viable in the new + population. + </para> + </section> + <section> + <title>Islands in the Watchmaker Framework</title> + <indexterm><primary><classname>IslandEvolution</classname></primary></indexterm> + <para> + The Watchmaker Framework for Evolutionary Computation supports islands models via the + <classname>IslandEvolution</classname> class. Each island is a self-contained + <classname>EvolutionEngine</classname> just like those we have been using previously for single-population + evolutionary algorithms. The evolution is divided into <emphasis>epochs</emphasis>. Each epoch consists + of a fixed number of generations that each island completes in isolation. At the end of an epoch migration + occurs. Then, if the termination conditions are not yet satisfied, a new epoch begins. + </para> + <para> + The <classname>IslandEvolution</classname> supports pluggable migration strategies via different implementations + of the <interfacename>Migration</interfacename> interface. An island version of the string evolution example + from <xref linkend="watchmaker_chapter" /> might look something like this: + </para> + <indexterm><primary><interfacename>Migration</interfacename></primary></indexterm> + <indexterm><primary><classname>RingMigration</classname></primary></indexterm> + <informalexample> + <programlisting language="java"> +<![CDATA[IslandEvolution<String> engine + = new IslandEvolution<String>(5, // Number of islands. + new RingMigration(), + candidateFactory, + evolutionaryOperator, + fitnessEvaluator, + selectionStrategy, + rng); + +engine.evolve(100, // Population size per island. + 5, // Elitism for each island. + 50, // Epoch length (no. generations). + 3, // Migrations from each island at each epoch. + new TargetFitness(0, false));]]> + </programlisting> + </informalexample> + <indexterm><primary><interfacename>IslandEvolutionObserver</interfacename></primary></indexterm> + <indexterm><primary><methodname>populationUpdate</methodname></primary></indexterm> + <indexterm><primary><methodname>islandPopulationUpdate</methodname></primary></indexterm> + <para> + We can add listeners to an <classname>IslandEvolution</classname> object, just as we can with individual + <interfacename>EvolutionEngine</interfacename>s. We use a different interface for this though, + <interfacename>IslandEvolutionObserver</interfacename>, which provides two call-backs. + The <methodname>populationUpdate</methodname> method reports the global state of the combined population + of all islands at the end of each epoch. The <methodname>islandPopulationUpdate</methodname> method reports + the state of individual island populations at the end of each generation. + </para> + <section> + <title>Advanced Usage</title> + <indexterm><primary><classname>GenerationalEvolutionEngine</classname></primary><secondary>island evolution</secondary></indexterm> + <para> + In the example code above we specified how many islands we wanted to use and the + <classname>IslandEvolution</classname> class created one <classname>GenerationalEvolutionEngine</classname> + for each island. Using this approach all of the islands have the same configuration; they use the same + candidate factory, evolutionary operator(s) and selection strategy. This is the easiest way to create an + island system but it is also possible to construct each island individually for ultimate flexibility. + </para> + <informalexample> + <programlisting language="java"> +<![CDATA[List<EvolutionEngine<String>> islands + = new ArrayList<EvolutionEngine<String>>(); + +// Create individual islands here and add them to the list. +// ... + +IslandEvolution<String> engine + = new IslandEvolution<String>(islands, + new RingMigration(), + false, // Natural fitness? + rng);]]> + </programlisting> + </informalexample> + <para> + One reason you might choose to construct the islands explicitly is that it makes it possible to configure + individual islands differently. You may choose to have different islands use different parameters + for evolutionary operators, or even to use different evolutionary operators all together. Alternatively, + you could use the same evolutionary operators and parameters but have different selection strategies so that + some islands have stronger selection pressure than others. You should generally use the same fitness function + for all islands though, otherwise you might get some strange results. + </para> + <indexterm><primary><classname>SteadyStateEvolutionEngine</classname></primary><secondary>island evolution</secondary></indexterm> + <indexterm><primary><classname>EvolutionStrategyEngine</classname></primary><secondary>island evolution</secondary></indexterm> + <para> + Another possible reason for creating the islands explicitly is so you don't have to use the standard + <classname>GenerationalEvolutionEngine</classname> for the islands. You can choose to use any implementation + of the <interfacename>EvolutionEngine</interfacename> interface, such as the + <classname>SteadyStateEvolutionEngine</classname> class or the <classname>EvolutionStrategyEngine</classname> + class. You can even use a mixture of different island types with the same + <classname>IslandEvolution</classname> object. + </para> + </section> + </section> +</chapter> |