Last week I went to Budapest to present the paper “An Ant-Based Rule for UMDA’s Update Strategy” in the 10th European Conference on Artificial Life (ECAL 2009). ECAL is one of the leading congresses in the area and some of the most relevant work in the Artificial Life research field is presented there in first hand. It is held every two years and this time the capital of Hungary was chosen to host the event. The Academy of Sciences, in Roosevelt tér (square), on the banks of the Danube and with a perfect view on the Castle and the hills of Buda was ECAL’s headquarters for 4 days.
Only 30% of the accepted papers were selected for oral presentation. The remaining was scheduled for poster sessions (although all the accepted papers were published in full-length in two LNCS volumes) that lasted…the whole day! I cannot understand why not all the congresses follow a line similar to PPSN (a poster-only congress, with 90 minutes sessions) when it comes to poster sessions, but ECAL’s strategy is, my opinion, particularly ineffective and exhausting.
ECAL 2009, Budapest, Academy of Sciences
As for our paper, it presents a study on an alternative update strategy for the Univariate Marginal Distribution Algorithm based on the ACO computational paradigm and first presented here. The aim is to control the balance between exploration and exploitation in order to avoid diversity loss, reduce the optimal population size and improve the scalability of the algorithm on hard problems. The results confirmed the hypothesis. This is the abstract:
This paper investigates an update strategy for the Univariate Marginal Distribution Algorithm (UMDA) probabilistic model inspired by the equations of the Ant Colony Optimization (ACO) computational paradigm. By adapting ACO’s transition probability equations to the univariate probabilistic model, it is possible to control the balance between exploration and exploitation by tuning a single parameter. It is expected that a proper balance can improve the scalability of the algorithm on hard problems with bounded difficulties and experiments conducted on such problems with increasing difficulty and size confirmed these assumptions. These are important results because the performance is improved without increasing the complexity of the model, which is known to have a considerable computational effort.
Despite the intuition that the same population size is not needed throughout the run of an Evolutionary Algorithm (EA), most EAs use a fixed population size.
This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing (SVPS) scheme on the performance of Genetic Algorithms (GAs). It consists in decreasing the population for a GA run following a predetermined schedule, configured by a speed and a severity parameter. The method uses as initial population size an estimation of the minimum size needed to supply enough building blocks, using a fixed-size selectorecombinative GA converging within some confidence interval toward good solutions for a particular problem. Following this methodology, a scalability analysis is conducted on deceptive, quasi-deceptive, and non-deceptive trap functions in order to assess whether SVPS-GA improves performances compared to a fixed-size GA under different problem instances and difficulty levels. Results show several combinations of speed-severity where SVPS-GA preserves the solution quality while improving performances, by reducing the number of evaluations needed for success.
In this paper we analyse the robustness of a Peer-to-Peer (P2P) Evolutionary Algorithm (EA) subject to the following dynamics: peers leave the system independently from each other causing a collective effect known as churn. The algorithm has been designed to tackle large instances of computationally expensive problems and, in this paper, we will assess its behavior under churn. To that end, we have performed a scalability analysis in five different scenarios using the Massively Multimodal Deceptive Problem as a
benchmark. In all cases, the P2P EA reaches the success criterion without a penalty on the response time. The key to the algorithm robustness is to ensure enough peers at the beginning of the experiment. Some of them leave but those that remain are enough to guarantee a reliable convergence.
The realization of a control unit can be done using a complex circuitry or microprogramming. The latter may be considered as an alternative method of implementation of machine instructions that can reduce the complexity and increase the flexibility of the control unit. The microcode efficiency and speed are of vital importance for the computer to execute machine instructions fast. This is a difficult task and it requires expert knowledge. It would be interesting and very helpful to have automated tools that, given a machine instruction description, could generate an efficient and correct microprogram. A good option is to use evolutionary computation techniques, which have proved been effective in the evolution of computer programs. In this paper, we intend to show how evolutionary computing techniques could be used to face this problem of generating efficient microprograms. We have developed a microarchitecture simulator of a real machine in order to evaluate an individual and to assign it the fitness value (to determine whether this candidate solution correctly implements the instruction machine). The proposed method is successful in generating correct solutions, not only for the machine code instruction set, but for new machine instructions not included in such set. We have shown that our approach can generate microprogramms to execute (to schedule microinstructions) the machine level instructions for a real machine. Moreover, this evolutive method could be applied to any microarchitecture just by changing the microinstruction set and pre-conditions of each machine instruction to guide evolution.
The poster, the source code of the proposed method and the full-length paper are available for download at http://atc.ugr.es/pedro/ev-micropr
Long time before coming back here. This Friday has been devoted to talking about XMPP and its research possibilities. It’s already a distributed infrastructure, so it wouldn’t be too difficult to turn it into a distributed computing infrastructure, which we are about to do.
We also got kind of lucky in our latest paper submissions, and got 5 papers accepted for CEC (within WCCI), plus two papers in EvoStar. We’ll be in Napoli to present them come late March.
