Waseda University · Computer Science
Professor Victor Parque's research lab specializes in the mathematical and computational foundations of graph representations, optimization, and control systems. The lab focuses on developing succinct, canonical, and efficient encodings for labeled graphs—particularly directed and loopy graphs—enabling applications in machine learning, network topology optimization, and autonomous systems. A key research direction involves leveraging evolutionary computation, such as Differential Evolution, for path planning and control system tuning, especially in mobile robotics and PID controller optimization under tight computational constraints. The lab also explores minimal-length tree layouts in polygonal environments, contributing to efficient networked system design across multi-agent and distributed frameworks.
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Mobile robots have recently attracted the attention and applicability in field areas ubiquitously. Within the context of autonomous navigation, path planning is relevant for comfortability, safety, execution time and energy savings. In this paper, we propose an approach to suggest smooth paths from observed robot trajectories by optimizing fitting and smoothness criteria using Differential Evolution with distinct modes of initialization, selection pressure, exploration and exploitation. Our rigo
Directed graphs encode meaningful dependencies among objects ubiquitously. This paper introduces new and simple representations for labeled directed graphs with the properties of being succinct (space is information-theoretically optimal); in which we avoid exploiting a-priori knowledge on digraph regularity such as triangularity, separability, planarity, symmetry and sparsity. Our results have direct implications to model directed graphs by using single integer numbers effectively, which is sig
Graphs denote useful dependencies among objects ubiquitously. This paper introduces new and simple bijections to the integer grid to enable the succinct, canonical and efficient representations of labeled graphs; whereas previous work has focused on regularities in structure such as triangularity, separability, planarity, symmetry and sparsity. By succinct we imply that space is information-theoretically optimal, by canonical we imply that generation of instances is unique, and by efficient we i
Graphs with self-loops enable to represent a large variety of interactions in natural and artificial systems, allowing not only inter-connectivity among heterogeneous entities but also the self-dependence of entities, e.g. the recursive and autonomous nature of dynamical systems. In this paper we present new bijective constructs which enable the numerical representation of graphs with self loops (or loopy graphs). In particular, we study the case of (1) undirected and (2) directed graphs with n
Optimal topologies in networked systems is of relevant interest to integrate and coordinate multi-agency. Our interest in this paper is to compute the root location and the topology of minimal-length tree layouts given n nodes in a polygonal map, assuming an n-star network topology. Computational experiments involving 600 minimal tree planning scenarios show the feasibility and efficiency of the proposed approach.
Proportional-Integral-Derivative (PID) is a simple and intuitive feedback-based control mechanism being useful to track set points and to reject disturbances. A key question in gradient-free optimization is to ascertain whether the class of optimization algorithms based on the difference of vectors generalize reasonably well to tackle a large class of PID control problems. For generalization and practical purposes, it would be desirable to render algorithms being able to tune PID controllers ove
Being a significant construct in a wide range of combinatorial problems, the k-subset sum problem (k-SSP) computes k-element subsets, out of an n-element set, satisfying a user-defined aggregation value. In this paper, we formulate the k-subset sum problem as a search (optimization) problem over the space of integers associated with combination elements. And by using rigorous computational experiments using the search space over more than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xm
This paper aims at computing minimal-length tree layouts given an n-star graph in a polygonal map. This problem is strongly related to the edge bundling problem, which consists of compounding the edges of an input graph to obtain topologically compact graph layouts being free of clutter and easy to visualize. Computational experiments using a diverse set of polygonal maps and number of edges in the input graph shows the feasibility, efficiency and robustness of our approach.
Modularity is vital to engineer complex products and machines. We assert that modularity can emerge in the context of desirable structures constrained to life cycle factors; and propose a method to evaluate machine modularity in the context of life cycle optimization. Experiments using explorit, a new and well suited global optimization algorithm, on fve relevant and divergent machine models show that it is possible to obtain tractable modules within the context of life cycle factors.
Combinations of m out of n are ubiquitous to model a wide class of combinatorial problems. For an ordered sequence of combinations, the unranking function generates the combination associated to an integer number in the ordered sequence. In this paper, we present a new method for unranking combinations by using a gradient-based optimization approach. Exhaustive experiments within computable allowable limits confirmed the feasibility and efficiency of our proposed approach. Particularly, our algo
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