Archive for the ‘Evoluntionary’ Category

DEAP: Distributed Evolutionary Algorithms in Python

Monday, April 9th, 2012

DEAP: Distributed Evolutionary Algorithms in Python

From the website:

DEAP is intended to be an easy to use distributed evolutionary algorithm library in the Python language. Its two main components are modular and can be used separately. The first module is a Distributed Task Manager (DTM), which is intended to run on cluster of computers. The second part is the Evolutionary Algorithms in Python (EAP) framework.

Components

DTM

DTM is a distributed task manager that is able to spread workload over a buch of computers using a TCP or a MPI connection.

DTM include the following features:

  • Easy to use parallelization paradigms
  • Offers a similar interface to the Python's multiprocessing module
  • Basic load balancing algorithm
  • Works over mpi4py
  • Support for TCP communication manager

EAP

EAP is the evolutionary core of DEAP, it provides data structures, methods and tools to design any kind of evolutionary algorithm. It works in perfect harmony with DTM, allowing easy parallelization of any demanding evolutionary task.

EAP includes the following features:

  • Genetic algorithm using any imaginable representation
    • List, Array, Set, Dictionary, Tree, Numpy Array (tip revision), etc.
  • Genetic programing using prefix trees
    • Loosely typed, Strongly typed
    • Automatically defined functions
  • Evolution strategies (including CMA-ES)
  • Multi-objective optimisation (NSGA-II, SPEA-II)
  • Co-evolution of multiple populations
  • Parallelization of the evaluations (and more)
  • Hall of Fame of the best individuals that lived in the population
  • Checkpoints that take snapshots of a system regularly
  • Benchmarks module containing most common test functions
  • Genealogy of an evolution (that is compatible with NetworkX)
  • Examples of alternative algorithms : Particle Swarm Optimization, Differential Evolution

If you are interested in evolutionary approaches to data mining, not a bad place to start.

Posted in DEAP, Evoluntionary, Python | No Comments »

META’2012 International Conference on Metaheuristics and Nature Inspired Computing

Saturday, November 5th, 2011

META’2012 International Conference on Metaheuristics and Nature Inspired Computing

Dates:

  • Paper submission: May 15, 2012
  • Session/Tutorial submission: May 15, 2012
  • Paper notification: July 15, 2012
  • Session/Tutorial notification: June 15, 2012
  • Conference: October 27-31, 2012

From the website:

The 4th International Conference on Metaheuristics and Nature Inspired Computing, META’2012, will held in Port El-Kantaoiui (Sousse, Tunisia).

The Conference will be an exchange space thanks to the sessions of the research works presentations and also will integrate tutorials and a vocational training of metaheuristics and nature inspired computing.

The scope of the META’2012 conference includes, but is not limited to:

  • Local search, tabu search, simulated annealing, VNS, ILS, …
  • Evolutionary algorithms, swarm optimization, scatter search, …
  • Emergent nature inspired algorithms: quantum computing, artificial immune systems, bee colony, DNA computing, …
  • Parallel algorithms and hybrid methods with metaheuristics, machine learning, game theory, mathematical programming, constraint programming, co-evolutionary, …
  • Application to: logistics and transportation, telecommunications, scheduling, data mining, engineering design, bioinformatics, …
  • Theory of metaheuristics, landscape analysis, convergence, problem difficulty, very large neighbourhoods, …
  • Application to multi-objective optimization
  • Application in dynamic optimization, problems with uncertainty,bi-level optimization, …

The “proceedings” for Meta ’10 can be seen at: Meta ’10 papers. It would be more accurate to say “extended abstracts” because, for example,

Luis Filipe de Mello Santos, Daniel Madeira, Esteban Clua, Simone Martins and Alexandre Plastino. A parallel GRASP resolution for a GPU architecture

runs all of two (2) pages. As is about the average length of the other twenty (20) papers that I checked.

I like concise writing but two pages to describe a parallel GRASP setup on a GPU architecture? Just an enticement (there is an ugly word I could use) to get you to read the ISI journal with the article.

Conference and its content look very interesting. Can’t say I care for the marketing technique for the journals in question. Not objecting to the marketing of the journals, but don’t say proceedings when what is meant is ads for the journals.

Posted in Artificial Intelligence, Constraint Programming, Emergent Semantics, Evoluntionary, Game Theory, Local Search, Metaheuristics, Tabu Search | No Comments »

Modeling Network Evolution Using Graph Motifs

Sunday, May 8th, 2011

Modeling Network Evolution Using Graph Motifs by Drew Conway.

Abstract:

Network structures are extremely important to the study of political science. Much of the data in its subfields are naturally represented as networks. This includes trade, diplomatic and conflict relationships. The social structure of several organization is also of interest to many researchers, such as the affiliations of legislators or the relationships among terrorist. A key aspect of studying social networks is understanding the evolutionary dynamics and the mechanism by which these structures grow and change over time. While current methods are well suited to describe static features of networks, they are less capable of specifying models of change and simulating network evolution. In the following paper I present a new method for modeling network growth and evolution. This method relies on graph motifs to generate simulated network data with particular structural characteristic. This technique departs notably from current methods both in form and function. Rather than a closed-form model, or stochastic implementation from a single class of graphs, the proposed “graph motif model” provides a framework for building flexible and complex models of network evolution. The paper proceeds as follows: first a brief review of the current literature on network modeling is provided to place the graph motif model in context. Next, the graph motif model is introduced, and a simple example is provided. As a proof of concept, three classic random graph models are recovered using the graph motif modeling method: the Erdos-Renyi binomial random graph, the Watts-Strogatz “small world” model, and the Barabasi-Albert preferential attachment model. In the final section I discuss the results of these simulations and subsequent advantage and disadvantages presented by using this technique to model social networks.

Now there’s an interesting idea.

Modeling the evolution of topic maps.

I wonder if the more interesting evolution would reflect the authoring of the topic map or the subject matter of the topic map?

I suppose that would depend upon the author and/or the subject of the map.

Update:

Graph Motif Modeling software now available as distributed Python package

Graph Motif Model Documentation

Posted in Evoluntionary, Graph Motif Model, Graphs | No Comments »

evo*2011

Thursday, March 10th, 2011

evo*2011

From the website:

evo* comprises the premier co-located conferences in the field of Evolutionary Computing: eurogp, evocop, evobio and evoapplications.

Featuring the latest in theoretical and applied research, evo* topics include recent genetic programming challenges, evolutionary and other meta-heuristic approaches for combinatorial optimization, evolutionary algorithms, machine learning and data mining techniques in the biosciences, in numerical optimization, in music and art domains, in image analysis and signal processing, in hardware optimization and in a wide range of applications to scientific, industrial, financial and other real-world problems.

Conference is 27-29 April 2011 in Torino, Italy.

Even if you are not in the neighborhood, the paper abstracts make an interesting read!

Posted in Data Mining, Evoluntionary, Machine Learning | No Comments »

As Time Goes by: Discovering Eras in Evolving Social Networks

Friday, November 12th, 2010

As Time Goes by: Discovering Eras in Evolving Social Networks Authors(s): Michele Berlingerio, Michele Coscia, Fosca Giannotti, Anna Monreale, Dino Pedreschi

Abstract:

Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus instead on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset; we illustrate how the discovered temporal clustering highlights the crucial moments when the network had profound changes in its structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.

Deeply interesting work.

Questions:

  1. Is is a fair assumption that terms used by one scholar will be used the same way by scholars that cite them? (discussion)
  2. If you think #1 is true, then does entity resolution, etc., however you want to talk about recognition of subjects, apply from the first scholar outwards? If so, how far? (discussion)
  3. If you think #1 is false, why? (discussion)
  4. How would you go about designing a project to identify usages of terms in a body of literature? Such that you could detect changes in usage? What questions would you have to ask? (3-5 pages, citations)

PS: Another way to think about this area is: Do terms have social lives? Is that a useful way to talk about them?

Posted in Clustering, Data Mining, Evoluntionary | No Comments »

A Survey of Genetics-based Machine Learning

Thursday, October 21st, 2010

A Survey of Genetics-based Machine Learning Author: Tim Kovacs

Abstract:

This is a survey of the field of Genetics-based Machine Learning (GBML): the application of evolutionary algorithms to machine learning. We assume readers are familiar with evolutionary algorithms and their application to optimisation problems, but not necessarily with machine learning. We briefly outline the scope of machine learning, introduce the more specific area of supervised learning, contrast it with optimisation and present arguments for and against GBML. Next we introduce a framework for GBML which includes ways of classifying GBML algorithms and a discussion of the interaction between learning and evolution. We then review the following areas with emphasis on their evolutionary aspects: GBML for sub-problems of learning, genetic programming, evolving ensembles, evolving neural networks, learning classifier systems, and genetic fuzzy systems.

The author’s preprint has 322 references. Plus there are slides, bibliographies in BibTeX.

If you are interesting in augmented topic map authoring using GBML, this would be a good starting place.

Questions:

  1. Pick 3 subject areas. What arguments would you make in favor of GBML for augmenting authoring of a topic map for those subject areas?
  2. Same subject areas, but what arguments would you make against the use of GBML for augmenting authoring of a topic map for those subject areas?
  3. Design an experiment to test one of your arguments for and against GBML. (project, use of the literature encouraged)
  4. Convert the BibTeX formatted bibliographies into a topic map. (project)

Posted in Evoluntionary, Learning Classifier, Machine Learning, Neural Networks | No Comments »

Evolutionary Clustering and Analysis of Heterogeneous Information Networks

Saturday, October 9th, 2010

Evolutionary Clustering and Analysis of Heterogeneous Information Networks Authors: Manish Gupta; Charu Aggarwal; Jiawei Han; Yizhou Sun Keywords: ENetClus, evolutionary clustering, typed-clustering, DBLP, bibliographic networks

Abstract:

In this paper, we study the problem of evolutionary clustering of multi-typed objects in a heterogeneous bibliographic network. The traditional methods of homogeneous clustering methods do not result in a good typed-clustering. The design of heterogeneous methods for clustering can help us better understand the evolution of each of the types apart from the evolution of the network as a whole. In fact, the problem of clustering and evolution diagnosis are closely related because of the ability of the clustering process to summarize the network and provide insights into the changes in the objects over time. We present such a tightly integrated method for clustering and evolution diagnosis of heterogeneous bibliographic information networks. We present an algorithm, ENetClus, which performs such an agglomerative evolutionary clustering which is able to show variations in the clusters over time with a temporal smoothness approach. Previous work on clustering networks is either based on homogeneous graphs with evolution, or it does not account for evolution in the process of clustering heterogeneous networks. This paper provides the first framework for evolution-sensitive clustering and diagnosis of heterogeneous information networks. The ENetClus algorithm generates consistent typed-clusterings across time, which can be used for further evolution diagnosis and insights. The framework of the algorithm is specifically designed in order to facilitate insights about the evolution process. We use this technique in order to provide novel insights about bibliographic information networks.

Exploring heterogeneous information networks is a first step towards discovery/recognition of new subjects. What other novel insights will emerge from work on heterogeneous information networks only future research can answer.

Posted in Clustering, Evoluntionary, Heterogeneous Data, Networks | No Comments »