テキスト分類のための進化的アルゴリズムを用いた機械学習最適化

熊本大

Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better resul

NusaCrowd: Open Source Initiative for Indonesian NLP Resources

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken

Optimasi Komposisi Pakan Sapi Menggunakan Hibridisasi Modifikasi Evolution Strategies Dan Linear Programming

Pada penggemukan sapi, salah satu kendala terbesar yang dihadapi adalah tingginya biaya pakan ternak. Sehingga peternak harus mampu merumuskan pakan ternak yang sesuai kebutuhan gizi sapi dengan biaya minimal untuk memaksimalkan pendapatan. Formulasi pakan menjadi rumit karena ada banyak hal yang harus diperhatikan. Kesalahan dalam menentukan formulasi pakan dapat mengakibatkan peningkatan biaya pakan dan hasil yang tidak optimal pada penggemukan sapi. Optimasi pakan sapi termasuk ke dalam kelas constrained optimization. Berbagai algoritma heuristik dan deterministik telah diterapkan untuk memecahkan permasalahan constrained optimization, maupun dalam optimasi komposisi pakan ternak. Namun, algoritma-algoritma tersebut masih belum stabil untuk menemukan solusi yang mendekati global optima. Oleh karena itu, penelitian ini mengusulkan modifikasi algoritma Evolution Strategies (ES) yang dihibridisasi menggunakan Linear Programming (LP). Parameter optimal untuk modifikasi Evolution Strategies, antara lain tipe ES yang digunakan adalah ES (μ/ρ+λ), jumlah populasi sebesar 500, jumlah offspring sebesar 25μ, metode rekombinasi discrete recombination, metode mutasi self-adaptation, metode seleksi elitist selection, modifikasi gen negatif menggunakan random injection, dan maksimal generasi sebesar 450. Terdapat dua model hibridisasi pada penelitian ini, yaitu hibridisasi Linear Programming-Modifikasi Evolution Strategies (LPMES) dan hibridisasi Modifikasi Evolution Strategies-Linear Programming (MESLP). Gabungan dari kedua model hibridisasi menghasilkan nilai rata-rata fitness tertinggi, yaitu sebesar 0.043722611 dengan rata-rata biaya sebesar Rp228.712,465 untuk 12 bahan pakan dan 18 ekor sapi dengan bobot awal dan Pertambahan Bobot Badan Harian (PBBH) yang berbeda-beda. MESLP memberikan hasil yang lebih baik dibandingkan ES, LP, random search, algoritma genetika, dan aplikasi formulasi pakan (Winfeed dan FeedLive). Hibridisasi modifikasi ES dan LP mampu menghasilkan rata-rata fitness tertinggi dengan ratarata biaya yang murah

Phrase Table Combination Based on Symmetrization of Word Alignment for Low-Resource Languages

Phrase table combination in pivot approaches can be an effective method to deal with low-resource language pairs. The common practice to generate phrase tables in pivot approaches is to use standard symmetrization, i.e., grow-diag-final-and. Although some researchers found that the use of non-standard symmetrization could improve bilingual evaluation understudy (BLEU) scores, the use of non-standard symmetrization has not been commonly employed in pivot approaches. In this study, we propose a strategy that uses the non-standard symmetrization of word alignment in phrase table combination. The appropriate symmetrization is selected based on the highest BLEU scores in each direct translation of source–target, source–pivot, and pivot–target of Kazakh–English (Kk–En) and Japanese–Indonesian (Ja–Id). Our experiments show that our proposed strategy outperforms the direct translation in Kk–En with absolute improvements of 0.35 (a 11.3% relative improvement) and 0.22 (a 6.4% relative improvement) BLEU points for 3-gram and 5-gram, respectively. The proposed strategy shows an absolute gain of up to 0.11 (a 0.9% relative improvement) BLEU points compared to direct translation for 3-gram in Ja–Id. Our proposed strategy using a small phrase table obtains better BLEU scores than a strategy using a large phrase table. The size of the target monolingual and feature function weight of the language model (LM) could reduce perplexity scores