Last week I was at EuroGP where I presented my latest paper “Automatic Design of Ant Algorithms with Grammatical Evolution” (pdf, slides) and it won the Best Paper Award! I am very happy with this distinction since EuroGP is the leading conference on Genetic Programming attended by the best researchers in the field.

In this paper, a Grammatical Evolution approach is used for the automatic design of Ant Colony Optimization algorithms. The used grammar has the ability to guide the learning of novel architectures, by rearranging components regularly found on manually designed variants (for example, the Elitist Ant-System, the Ant Colony System or the Max-Min Ant System). This approach was tested with the TSP and the results show that the evolved algorithmic strategies are effective, exhibit a good generalization capability and are competitive with human designed variants. This is still a starting point and there is a large amount of work to be done but the indications given by these results are encouraging!

This year the conference took place in Málaga, Spain, as part of Evo* as usual. The event was great with an excellent organization lead by Carlos Cotta. It was a fantastic Evo*! Next year it will be held in Wien!

Just received an email with good news! The paper that I got accepted at EuroGP was nominated for Best Paper! The work that I started last year is starting to get some good feedback which is great :-) This paper describes the approach I have developed (in collaboration with Francisco B. Pereira) to evolve pheromone update methods for Ant Systems. The paper only deals with the TSP and the obtained results are promising since it outperforms MMAS. It is the first step to what I am working on, a Self-Ant System. The idea is to let the Ant algorithm find the best components that can help in solving the problem at hand. There is still a lot of work to be done but this is just the start.

I will be talking about my lastest work which ties together two techniques that I’ve always wanted to do some serious work with them: Ant Systems and Genetic Programming. In this first paper, I am using GP to evolve an Ant System component. A key issue in AS research is how to design the communication mechanism between ants that allows them to effectively solve a problem. We propose in this work to evolve the current pheromone trail update methods. We tested with the TSP and initial results show that the evolved strategies perform well and exhibit a good generalization capability when applied to larger instances.

Doing this work was also very fun and it was all made in Lisp. I just need to improve the code a bit and package it nicely before making it free available.

Anyway, I wish PPSN 2010 will be an interesting conference!

Dalam dunia matematika dan pemrograman, terdapat berbagai algoritma untuk pencarian rute optimal. Salah satu algoritma yang kini sedang berkembang pesat adalah Ant System, yang dikembangkan dari analogi koloni semut, yang mencari makanan dengan bantuan cairan kimia bernama pheromone. Untuk mengetahui kelebihan dan kekurangan dari algortima ini, algoritma ini dibandingkan dengan algoritma Transitive Closure. Tugas Akhir ini bertujuan untuk membuat suatu aplikasi perbandingan antara algoritma Ant System dengan algoritma Transitive Closure. Data jalan untuk pencarian rute didapat dengan pengolahan citra 2-dimensi menjadi data vektor dalam bentuk graph. Hal ini dapat dilakukan dengan fungsi dilatasi dan skeletonization. Aplikasi ini dibuat menggunakan Borland Delphi 7. Hasil pengujian aplikasi menunjukkan bahwa algoritma Ant System dan algoritma Transitive Closure dapat menemukan sebuah rute dari titik awal ke titik tujuan. Dari hasil pengujian antara kedua algoritma ini, dapat disimpulkan bahwa baik dari segi akurasi maupun dari segi durasi proses, algoritma Transitive Closure lebih baik daripada algoritma Ant System. Ini disebabkan karena algoritma Ant System merupakan sebuah general-purpose heuristic, sedangkan algoritma Transitive Closure merupakan sebuah dynamic programming. Kedua buah algoritma ini sangat berbeda dari segi paradigma dan tujuan, sehingga kurang sesuai untuk dibandingkan.

Keyword : optimal path finding, ant system, transitive closure

Sumber : http://repository.petra.ac.id/8041/

Persoalan pencarian lokasi terdekat ini pada prinsipnya didasarkan pada proses pencarian rute terdekat atau biasa disebut dengan Traveling Salesman Problem (TSP). Umumnya pemecahan permasalahan ini digunakan untuk efisiensi waktu dan biaya. Dalam penelitian ini, penulis membuat prototipe aplikasi pencarian lokasi terdekat menggunakan SMS dengan pendekatan salah satu dari kumpulan algoritma Ant Colony Optimization (ACO) yaitu Ant System. Hasil uji coba sistem yang diaplikasikan pada kota Surabaya menunjukan aplikasi ini dapat memberikan informasi lokasi terdekat beserta rute jalan terdekat untuk menuju ke lokasi tersebut yang dapat diakses melalui SMS.

Keyword : SMS, Ant Colony Optimization (ACO), Traveling Salesman Problem(TSP), Shortest Path, Ant System

Sumber : http://repository.petra.ac.id/59/