Gene Regulatory Networks (GRNs) inference from gene expression data is a hard task and a widely addressed challenge. GRNs can be represented as Boolean models similarly to digital circuits. Cartesian Genetic Programming (CGP), often used for designing circuits, can thus be adopted in the inference of GRNs. The main CGP operator for generating candidate designs is mutation, making its choice important for obtaining good results. Although there are many mutation operators for CGP, to the best of our knowledge, there is no analysis of them in the GRN inference problem. An evaluation of the Single ActiveMutation (SAM) and the Semantically-Oriented Mutation Operator (SOMO)is performed here for GRNs inference. Also, a combination of both operators is proposed. We use a benchmark single-cell RNA-Sequencingtime series data and its evaluation pipeline to measure the performance of the approaches. The experiments indicate that (i) combining SOMOand SAM provides the best results, and (ii) the results obtained by the proposal are competitive with those from state-of-the-art methods.

On the Analysis of CGP Mutation Operators when Inferring Gene Regulatory Networks using scRNA-Seq Time Series Data

Approximate Computing is an emerging paradigm that takes advantage of inherently error resilient digital circuits to design circuits with higher energetic efficiency, lower delay, or a smaller occupied area on the chips. Traditional approaches do not handle multiple objectives and metaheuristics appear as a proper alternative. In particular, Multiobjective Cartesian Genetic Programming (MOCGP) can find good solutions to the design and optimization of approximate circuits. The performance of CGP depends on the mutation adopted, as normally CGP only uses mutation for creating new solutions. However, to the best of our knowledge, just the traditional point mutation (PM) was used by the previously proposed MOCGP. Thus, the literature lacks an analysis of the best mutation operators of MOCGP. We propose here the analysis of PM and Single Active Mutation(SAM) on the multiobjective optimization of 15 heterogeneous combinational logic circuits from scratch and starting with a feasible solution. The results indicate that SAM obtained better results than PM.

On the Analysis of Mutation Operators in Multiobjective Cartesian Genetic Programming for designing Combinational Logic Circuits

The design of digital logic circuits involves transforming logical expressions into an electronic circuit seeking to minimize certain attributes such as the number of transistors. There are deterministic algorithms for this task, but they are limited to small problems. On the other hand metaheuristics can be used for solving this type of problem and Cartesian Genetic Programming (CGP) is widely adopted in the literature. In CGP, mutation is commonly the only operator for generating new candidate solutions. Thus, the performance of this metaheuristic is dependent on the proper choice of mutation and its parameters. Normally, CGP mutates the candidate solutions according to a uniform distribution. Thus, any modification has the same chance to occur. In order to improve the performance of CGP an adaptive mutation operator using an $\epsilon$-greedy strategy for bias the selection of gates is proposed here. The proposal is evaluated using problems of a benchmark from the literature. The results obtained indicate that the proposed adaptive mutation is promising and that its relative performance increases with the size of the problem.

A Reinforcement Learning Based Adaptive Mutation for Cartesian Genetic Programming Applied to the Design of Combinational Logic Circuits

In the Multi-Agent Pickup and Delivery (MAPD) problem, agents must process a sequence of tasks that may appear in the system in different time-steps. Commonly, this problem has two parts: (i) task allocation, where the agent receives the appropriate task, and (ii) path planning, where the best path for the agent to perform its task, without colliding with other agents, is defined. In this work, we propose an integer-encoded genetic algorithm for solving the task allocation part of the MAPD problem combined with two-path planning algorithms already known in the literature: the Prioritized Planning and the Improved Conflict-Based Search (ICBS). Computational experiments were carried out with different numbers of agents and the frequency of tasks. The results show that the proposed approach achieves better results for large instances when compared to another technique from the literature.

Solving Multi-Agent Pickup and Delivery Problems Using a Genetic Algorithm


 **Chairs:** Andre Britto	and	Karina Valdivia
 

Metaheuristics and Optimization I

technical paper

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

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BRACIS 2021

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

ENIAC: Technical Session 1

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

Heder Soares Bernardino

4

1

Presentations

On the Analysis of CGP Mutation Operators when Inferring Gene Regulatory Networks using scRNA-Seq Time Series Data

On the Analysis of Mutation Operators in Multiobjective Cartesian Genetic Programming for designing Combinational Logic Circuits

A Reinforcement Learning Based Adaptive Mutation for Cartesian Genetic Programming Applied to the Design of Combinational Logic Circuits

Solving Multi-Agent Pickup and Delivery Problems Using a Genetic Algorithm

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