14 results on '"Osaba, Eneko"'
Search Results
2. Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis
- Author
-
Zhang, Xiao, Onieva, Enrique, Perallos, Asier, and Osaba, Eneko
- Abstract
Accurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently.
- Published
- 2020
- Full Text
- View/download PDF
3. Bio-inspired computation: Where we stand and what's next.
- Author
-
Del Ser, Javier, Osaba, Eneko, Molina, Daniel, Yang, Xin-She, Salcedo-Sanz, Sancho, Camacho, David, Das, Swagatam, Suganthan, Ponnuthurai N., Coello Coello, Carlos A., and Herrera, Francisco
- Subjects
SOCIAL facts ,BIOLOGICALLY inspired computing ,SCIENTIFIC community ,MATHEMATICAL optimization ,INDUSTRY 4.0 ,SOCIAL networks - Abstract
In recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
4. A Discrete and Improved Bat Algorithm for solving a medical goods distribution problem with pharmacological waste collection.
- Author
-
Osaba, Eneko, Yang, Xin-She, Fister, Iztok, Del Ser, Javier, Lopez-Garcia, Pedro, and Vazquez-Pardavila, Alejo J.
- Subjects
PHARMACOLOGY ,ALGORITHMS ,ALGEBRA ,MEDICAL sciences ,BIOPHARMACEUTICS - Abstract
Abstract The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
5. A 4-dimensional model and combined methodological approach to inclusive Urban planning and design for ALL.
- Author
-
Rebernik, Nataša, Goličnik Marušić, Barbara, Bahillo, Alfonso, and Osaba, Eneko
- Subjects
URBAN planning ,PUBLIC spaces ,ETHNOGRAPHIC analysis ,POST-occupancy evaluation (Architecture) ,COMPLEXITY (Philosophy) - Abstract
Highlights • The paper introduces the 4-dimensional model and a combined methodological approach (CMA) towards inclusive public spaces design. • The backbone of the CMA is a slow, small and deep-data oriented ethnographic research, complemented with POE and BM as two complementary methods. • The CMA has been designed based on four study cases conducted in Slovenia, bringing a set of contextual-based and methodology-based conclusions. • The conclusions show that the CMA can serve as a powerful tool for stakeholders to gain deeper understanding of (disabled) citizens and their needs. Abstract Due to the emerging complexity of cities, this paper argues for a holistic, integrative and relational approach to more inclusive city planning and design to fit the needs of citizens with diverse impairments. It proposes and tests a new theoretical model called the combined methodological approach (CMA). The backbone of this model is an often-overlooked qualitative, bottom-up-driven, slow, small and deep-data-oriented ethnographic research, combined with components or phases of post-occupancy evaluation and behavioural mapping as two user-oriented techniques for assessing usage-space relationships. The paper is rather theoretical, as it focuses on the argumentation of different approaches in city planning, design and governance. However, tests of the proposed model were conducted in public open spaces of four pilot cases in two European cities (Maribor and Ljubljana, in Slovenia). The proposed CMA was tested against its applicability to real urban environments. The results, in accordance with the methodology used, showed that such a combination of often closely related, overlapping and complementary techniques can significantly enhance the understanding of complex relations and interactions between people, space and technology within the city. Hence, it can empower stakeholders towards more informative and responsive measures – and, finally, more inclusive, individualized, tailor-made cities. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
6. A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem.
- Author
-
Osaba, Eneko, Ser, Javier Del, Sadollah, Ali, Bilbao, Miren Nekane, and Camacho, David
- Subjects
TRAVELING salesman problem ,HYDROLOGIC cycle ,SYMMETRIC functions ,AUTOMATIC differentiation ,GENETIC algorithms - Abstract
Highlights • An improved discrete water cycle algorithm is presented for the TSP and ATSP. • This version includes inclination feature, enhancing exploration and exploitation. • 33 datasets of the TSP/ATSP have been used for the experimentation. • Results have been compared with six different techniques. • Friedman's and Holm's post hoc statistical tests have been conducted. Abstract The water cycle algorithm (WCA) is a nature-inspired meta-heuristic recently contributed to the community in 2012, which finds its motivation in the natural surface runoff phase in water cycle process and on how streams and rivers flow into the sea. This method has been so far successfully applied to many engineering applications, spread over a wide variety of application fields. In this paper an enhanced discrete version of the WCA (coined as DWCA) is proposed for solving the Symmetric and Asymmetric Traveling Salesman Problem. Aimed at proving that the developed approach is a promising approximation method for solving this family of optimization problems, the designed solver has been tested over 33 problem datasets, comparing the obtained outcomes with the ones got by six different algorithmic counterparts from the related literature: genetic algorithm, island-based genetic algorithm, evolutionary simulated annealing, bat algorithm, firefly algorithm and imperialist competitive algorithm. Furthermore, the statistical significance of the performance gaps found in this benchmark is validated based on the results from non-parametric tests, not only in terms of optimality but also in regards to convergence speed. We conclude that the proposed DWCA approach outperforms – with statistical significance – any other optimization technique in the benchmark in terms of both computation metrics. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
7. Evolutionary Multitask Optimization: Fundamental research questions, practices, and directions for the future.
- Author
-
Osaba, Eneko, Del Ser, Javier, and Suganthan, Ponnuthurai N.
- Subjects
EVOLUTIONARY computation ,COMMUNITIES ,EVOLUTIONARY algorithms ,TRACK & field - Abstract
Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years. It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds. However, evidences observed in recent contributions confirm that there are critical aspects that are not properly addressed to date. This short communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved. Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization: (i) the plausibility and practical applicability of this paradigm; (ii) the novelty of some proposed multitasking methods; and (iii) the methodologies used for evaluating newly proposed multitasking algorithms. As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track. Our ultimate purpose is to unveil gaps in the current literature, so that prospective works can attempt to fix these gaps, avoiding to stumble on the same stones and eventually achieve valuable advances in the area. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
8. Editorial: Memetic Computing: Accelerating optimization heuristics with problem-dependent local search methods.
- Author
-
Osaba, Eneko, Del Ser, Javier, Cotta, Carlos, and Moscato, Pablo
- Subjects
EVOLUTIONARY computation ,HEURISTIC ,ALGORITHMS - Abstract
Memetic Algorithms and, in general, approaches underneath the wider Memetic Computing paradigm, have been at the core of a frantic research activity since the very inception of this research area in the late eighties. The community working in this area has so far showcased the benefits of hybridizing population-based algorithms with trajectory-based methods or any other specialized procedures that encompass problem-specific knowledge in a variety of real-world scenarios. From the perspective of the algorithms themselves, this hybridization can be realized in many different ways: it is this upsurge of manifold algorithmic approaches what has maintained a vigorous and intense activity around Memetic Computing over the years, progressively adapting the paradigm to newly emerging problem formulations and characteristics. This editorial introduces the readership of Swarm and Evolutionary Computation to the contributions finally included in the Special Issue on Memetic Computing: Accelerating Optimization Heuristics with Problem-Dependent Local Search Methods. The high quality of the works presented in this editorial unquestionably proves the excellent health of this vibrant research area, as well as its continued success at tackling challenging real-world optimization problems. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
9. A prescription of methodological guidelines for comparing bio-inspired optimization algorithms.
- Author
-
LaTorre, Antonio, Molina, Daniel, Osaba, Eneko, Poyatos, Javier, Del Ser, Javier, and Herrera, Francisco
- Subjects
MATHEMATICAL optimization ,ALGORITHMS ,SWARM intelligence ,EVOLUTIONARY computation ,MEDICAL prescriptions ,ONLINE algorithms - Abstract
Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
10. A multi–agent approach for dynamic production and distribution scheduling
- Author
-
Á?lvarez, Esther, Díaz, Fernando, and Osaba, Eneko
- Abstract
In an age when enterprises are increasingly dependent on their suppliers and customers, individual companies should broaden their scope in order to include other organisations participating in the same value chain. Consequently, different parties of the chain ought to exchange information to make sound decisions in order to cut down global costs or improve customer service. This paper proposes a multi–agent approach for dynamic production and distribution scheduling in a simple supply chain. The approach is based on the continuous supervision of the active schedules and routes, in order to detect possible exceptions and apply corrective actions in a real–time and coordinated manner with other parties. Finally, the paper focuses on the design phase of the distribution stage, where the problem is mathematically formulated as a dynamic vehicle routing problem with time windows and backhauls (DVRP–TWB), and three combinatorial optimisation meta–heuristics are proposed as solving techniques.
- Published
- 2014
- Full Text
- View/download PDF
11. A multi-crossover and adaptive island based population algorithm for solving routing problems
- Author
-
Osaba, Eneko, Onieva, Enrique, Carballedo, Roberto, Diaz, Fernando, Perallos, Asier, and Zhang, Xiao
- Abstract
We propose a multi-crossover and adaptive island based population algorithm (MAIPA). This technique divides the entire population into subpopulations, or demes, each with a different crossover function, which can be switched according to the efficiency. In addition, MAIPA reverses the philosophy of conventional genetic algorithms. It gives priority to the autonomous improvement of the individuals (at the mutation phase), and introduces dynamism in the crossover probability. Each subpopulation begins with a very low value of crossover probability, and then varies with the change of the current generation number and the search performance on recent generations. This mechanism helps prevent premature convergence. In this research, the effectiveness of this technique is tested using three well-known routing problems, i.e., the traveling salesman problem (TSP), capacitated vehicle routing problem (CVRP), and vehicle routing problem with backhauls (VRPB). MAIPA proves to be better than a traditional island based genetic algorithm for all these three problems.
- Published
- 2013
- Full Text
- View/download PDF
12. A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems.
- Author
-
Osaba, Eneko, Villar-Rodriguez, Esther, Del Ser, Javier, Nebro, Antonio J., Molina, Daniel, LaTorre, Antonio, Suganthan, Ponnuthurai N., Coello Coello, Carlos A., and Herrera, Francisco
- Subjects
MATHEMATICAL optimization ,METAHEURISTIC algorithms ,STATISTICAL significance ,EXPERIMENTAL design - Abstract
In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
13. Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics.
- Author
-
Osaba, Eneko, Del Ser, Javier, Camacho, David, Bilbao, Miren Nekane, and Yang, Xin-She
- Subjects
SOCIAL problems ,RANKING (Statistics) ,COMMUNITIES ,SEARCH algorithms ,PERFORMANCE theory - Abstract
Detecting groups within a set of interconnected nodes is a widely addressed problem that can model a diversity of applications. Unfortunately, detecting the optimal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to providing an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti–Fortunato–Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform competitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come. • An overview of community detection from the perspective of bioinspired computation. • Experimental study to assess the performance of seven modern heuristics. • 19 different solvers combining two similarity metrics and eight movement operators. • 36 different network instances have been used, from 35 to 600 nodes. • Envisioned status of this area, for which we identify challenges and opportunities. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
14. Soft Computing for Swarm Robotics: New Trends and Applications.
- Author
-
Osaba, Eneko, Del Ser, Javier, Iglesias, Andres, and Yang, Xin-She
- Subjects
AGGREGATION (Robotics) ,SOFT computing ,STRUCTURAL health monitoring ,SWARM intelligence ,EMERGENCY management ,COMPUTATIONAL intelligence - Abstract
Robotics have experienced a meteoric growth over the last decades, reaching unprecedented levels of distributed intelligence and self-autonomy. Today, a myriad of real-world scenarios can benefit from the application of robots, such as structural health monitoring, complex manufacturing, efficient logistics or disaster management. Related to this topic, there is a paradigm connected to Swarm Intelligence which is grasping significant interest from the Computational Intelligence community. This branch of knowledge is known as Swarm Robotics, which refers to the development of tools and techniques to ease the coordination of multiple small-sized robots towards the accomplishment of difficult tasks or missions in a collaborative fashion. The success of Swarm Robotics applications comes from the efficient use of smart sensing, communication and organization functionalities endowed to these small robots, which allow for collaborative information sensing, operation and knowledge inference from the environment. The numerous industrial and social applications that can be addressed efficiently by virtue of swarm robotics unleashes a vibrant research area focused on distributing intelligence among autonomous agents with simple behavioral rules and communication schedules, yet potentially capable of realizing the most complex tasks. In this context, we present and overview recent contributions reported around this paradigm, which serves as an exemplary excerpt of the potential of Swarm Robotics to become a major research catalyst of the Computational Intelligence arena in years to come. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.