# Claus AranhaUniversity of Tsukuba · Department of Computer Science

Claus Aranha

PhD

## About

83

Publications

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409

Citations

Citations since 2016

## Publications

Publications (83)

Metaheuristic Search is a successful strategy for solving optimization problems, leading to over two hundred published metaheuristic algorithms. Consequently, there is an interest in understanding the similarities between metaheuristics. Previous studies have done theoretical analyses based on components and search strategies, providing insights in...

We introduce Knowledge-Driven Program Synthesis (KDPS) as a variant of the program synthesis task that requires the agent to solve a sequence of program synthesis problems. In KDPS, the agent should use knowledge from the earlier problems to solve the later ones. We propose a novel method based on PushGP to solve the KDPS problem, which takes subpr...

The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These automatically designed metaheu...

Assisted seismic history matching (ASHM) is an optimisation problem that incorporates 4D seismic data as a constraint upon a reservoir simulation update. The observed and simulated three-dimensional seismic data is typically reduced to a Cartesian map representation and the misfit between the two is calculated using the mean squared error (MSE). Th...

Werewolf is a popular party game throughout the world, and research on its significance has progressed in recent years. The Werewolf game is based on conversation, and in order to win, participants must use all of their cognitive abilities. This communication game requires the playing agents to be very sophisticated to win. In this research, we gen...

Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective prob...

Seismic History Matching (SHM) is a key problem in the geosciences community, requiring optimal parameters of a subsurface model that match the observed data from multiple in-situ measurements. Therefore, the SHM problems are usually solved with Multi-Objective Evolutionary Algorithms (MOEAs). This group of algorithms optimize multiple objectives s...

The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These automatically designed metaheu...

The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resource Allocation metrics. Thus, it is still uncertain what the main factors are that lead to increments in perform...

Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective prob...

The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resource Allocation metrics. Thus, it is still uncertain what the main factors are that lead to increments in perform...

The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a popular algorithm for solving Multi-Objective Problems (MOPs). The main component of MOEA/D is to decompose a MOP into easier sub-problems using a set of weight vectors. The choice of the number of weight vectors significantly impacts the performance of MOEA/D. However,...

The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a popular algorithm for solving Multi-Objective Problems (MOPs). The main component of MOEA/D is to decompose a MOP into easier sub-problems using a set of weight vectors. The choice of the number of weight vectors significantly impacts the performance of MOEA/D. However,...

The automation of complex negotiations is required to coordinate various AIs. In complex negotiations, the goals of each agent may not be shared because some agents may benefit from not disclosing their own information during negotiation. This uncooperative situation requires an agent to have the ability to infer the intention of the others from th...

Finding good solutions for Multi-objective Problems (MOPs) is considered a hard problem, especially when considering MOPs with constraints. Thus, most of the works in the context of MOPs do not explore in-depth how different constraints affect the performance of MOP solvers. Here, we focus on exploring the effects of different Constraint Handling T...

Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints. Thus, most of the works in the context of MOPs do not explore in-depth how different constraints affect the performance of MOP solvers. Here, we focus on exploring the effects of different Constrai...

Cuckoo Search (CS) is a simple yet efficient swarm intelligence algorithm based on Lévy Flight. However, its performance can depend heavily on the parameter settings. Though many studies have designed control strategies for scaling factor \(\alpha \), few have considered the adaption of the stability parameter \(\beta \) (of Lévy Flight). In this p...

Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk. To solve this problem, recent studies applied multi-objective evolutionary algorithms (MOEAs) for its natural bi-objective structure. This paper presents a method injecting a distrib...

Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk. To solve this problem, recent studies applied multi-objective evolutionary algorithms (MOEAs) for its natural bi-objective structure. This paper presents a method injecting a distrib...

One of the main mechanisms of an optimization problem is the effectiveness and relevance of the objective function. In the context of an optimization problem in the subsurface domain, called seismic history matching, this study proposes to investigate further aspects of assimilating data. We focus on two main characteristics of the objective functi...

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easie...

Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of...

The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 pose...

One of the main algorithms for solving Multi-Objective Optimization Problems is the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). It is characterized by decomposing the multiple objectives into a large number of single-objective subproblems, and then solving these subproblems in parallel. Usually, these subproblems are con...

The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy, head injuries, dizziness, headaches and brain tumors. In this context, one important task is the identification of visible structures in the EEG signal, such as sleep sp...

The key characteristic of the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) is that a multi-objective problem is decomposed into multiple single-objective subproblems. In standard MOEA/D, all subproblems receive the same computational effort. However, as each subproblem relates to different areas of the objective space, it...

In the last 20 years, literally dozens of optimization algorithms based on swarm intelligence have been proposed. Particle Swarm Optimization, Artificial Bee Colony, Cuckoo Search, Firefly Optimization, and Cat Swarm Optimization are just a small sample of the exuberance of swarm-like algorithms. Although they differ in implementation details, they...

The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy, head injuries, dizziness, headaches and brain tumors. In this context, one important task is the identification of visible structures in the EEG signal, such as sleep sp...

In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks. The generation of artificial training data can be extremely useful in situations such as imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also useful when the data contains sensitive inf...

Recently, deep convolutional neural networks have shown good results for image recognition. In this paper, we use convolutional neural networks with a finder module, which discovers the important region for recognition and extracts that region. We propose applying our method to the recognition of protein crystals for X-ray structural analysis. In t...

We perform an experimental study about the effectof the tournament size parameter from the Tournament Selectionoperator. Tournament Selection is a classic operator for GeneticAlgorithms and Genetic Programming. It is simple to implementand has only one control parameter, thetournament size.Eventhough it is commonly used, most practitioners still re...

Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easie...

The Graph Coloring Problem is an important benchmark problem for decision and discrete optimization problems. In this work, we perform a comparative experimental study of four algorithms based on Swarm Intelligence for the 3-Graph Coloring Problem: Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Cuckoo Search (CS) and FireFly Algo...

We adapt the Cuckoo Search (CS) algorithm for solving the three color Graph Coloring Problem (3-GCP). The difficulty of this task is adapting CS from a continuous to a discrete domain. Previous researches used sigmoid functions to discretize the Lévi Flight (LF) operator characteristic of CS, but this approach does not take into account the concept...

We propose an agent-based simulation system for evacuations after earthquakes and tsunami. We focus on an improved model of communication among agents, by including a model of speech intelligibility in crowds, and the use of voice to change the evacuation-start behavior. This allows the model to represent the effects of evacuees calling to each oth...

Seismic history matching (SHM) has gained popularity as a technique to update a simulation model for field management (Roggero et al., 2013). Two common approaches for SHM is to update the simulation model based either on (1) its static seismic properties (Yin et al. 2015); or (2) its dynamic properties, through incorporation into the history match...

In the petroleum industry, accurate oil reservoir models are crucial in the decision making process. One critical step in reservoir modeling is History Matching (HM), where the parameters of a reservoir model are adjusted in order to improve its accuracy and enhance future prediction. Recent works applied evolutionary algorithms (EAs) such as GA, D...

Understanding the mechanisms and patterns of earthquake occurrence is of crucial importance for assessing and mitigating the seismic risk. In this work we analyze the viability of using Evolutionary Computation (EC) as a means of generating models for the occurrence of earthquakes. Our proposal is made in the context of the "Collaboratory for the S...

In this paper, we propose the hybrid application of two nature inspired approaches to the problem of Portfolio Optimization. This problem consists of the selection and weighting of financial assets. Its goal is to form an investment strategy which maximizes a return measure and minimizes a risk measure. We perform a series of simulation experiments...

Let us try to predict a time series. The goal here is to establish a function based on observed values (time series data). Using $$x_1,x_2,x_3,...,x_t,~~~~~~~(4.1)$$ we attempt to obtain a function $$x_t = f (x_{t-1},x_{t-2},x_{t-3},x_{t-4},...,x_{t-M})~~~~~~~ (4.2)$$ that can be used to predict current data x t from previously observed data. The r...

In the previous chapters we described how we can use Evolutionary Computation to perform forecasting in financial data and trend analysis. In both cases, computational intelligence, in the form of EC, processes large amounts of financial data, and transforms it into information that can be used by a human trader.
But what if we want to design a com...

One of the first concepts that a person learns when dealing with the market is that of “Buy Low, Sell High”. In other words, if the trader buys a stock which is expected to go up in price, he will make a profit if he sells that stock later. For example, let’s say that a car company A is planning to build a new factory this year. In a simple interpr...

In the first chapter we saw that Evolutionary Computation use the concepts of natural evolution to efficiently search the solution of an optimization problem.
In the previous chapter we described the basic concepts of Evolutionary Computation. We discussed the natural underpinnings of evolution that were used as motivation and guidelines for EC. Th...

Financial Engineering is the term used to describe the use of engineering and mathematical methods and tools to solve financial problems. This includes, but it is not limited to, the mathematical analysis of the market, the modeling of its behavior, and eventually the use of optimization methods on this model.
The goal of the financial engineer is...

In the previous chapter, we saw how to predict the future prices of an asset, based on its past prices. This problem was called “time series prediction”, and there are many different techniques, both traditional and evolutionary, to perform this task. All these techniques use the information provided by the past prices of the stock, called the hist...

We can take from the above expert that a large part of technological and social innovations come from improvements on already existing ideas. It could not be in any other way: the human being has an irresistible urge to explore the world around it and modify it, and this includes both the natural world, and that created by his ancestors.
However, j...

Portfolio Optimization (PO) is a resource allocation problem where real valued weights are assigned to multiple financial assets in order to maximize the return and minimize the risk. The Memetic Tree-based Algorithm (MTGA), employing a tree representation allied with local search (LS) has shown great performance compared to other weight balancing...

We present two applications of genetic programming to real world problems: musical composition and financial portfolio optimization.
In each of these applications, a specialized genome representation is used in order to break the problem down into smaller
instances and put them back together. Results showing the applicability of the approaches are...

We introduce a Memetic system to solve the application problem of Financial Portfolio Optimization. This problem consists
of selecting a number of assets from a market and their relative weights to form an investment strategy. These weights must
be optimized against a utility function that considers the expected return of each asset, and their co-v...

The Portfolio Optimization problem consists of the selection of a group of assets to a long-term fund in order to minimize the risk and maximize the return of the investment. This is a multi-objective (risk, return) resource allocation problem, where the aim is to correctly assign weights to the set of available assets, which determines the amount...

The generation of profitable trading rules for Foreign Exchange (FX) investments is a difficult but popular problem. The use of Machine Learning in this problem allows us to obtain objective results by using information of the past market behavior. In this paper, we propose a Genetic Algorithm (GA) system to automatically generate trading rules bas...

We propose a new evolutionary framework called MGVPC+ for the sake of solving classification problems. This approach is an extension of our earlier framework MVGPC, in which a majority voting genetic programming classifier was established with multiple GP runs. In this paper, we integrate GP-based classifiers and other machine learning techniques,...

We use local search to improve the performance of Genetic Algorithms applied the problem of Financial Portfolio Selection
and Optimization. Our work describes the Tree based Genetic Algorithm for Portfolio Optimization. To improve this evolutionary
system, we introduce a new guided crossover operator, which we call the BWS, and add a local optimiz...

Recently, a number of works have been done on how to use Genetic Algorithms to solve the Portfolio Optimization problem, which is an instance of the Resource Allocation problem class. Almost all these works use a similar genomic representation of the portfolio: An array, either real, where each element represents the weight of an asset in the portf...

Portfolio optimization by GA is a problem that has recently received a lot of attention. However, most works in this area have so far ignored the effects of cost on Portfolio Optimization, and haven't directly addressed the problem of portfolio management (continuous optimization of a portfolio over time). In this work, we use the Euclidean Distanc...