Spiking Neural Networks (SNNs) have shown substantial promise in processing spatio-temporal data, mimicking biological neuronal mechanisms, and saving computational power. However, most SNNs use fixed model regardless of their locations in the network. This limits SNNs’ capability of transmitting precise information in the network, which becomes worse for deeper SNNs. Some researchers try to use specified parametric models in different network layers or regions, but most still use preset or suboptimal parameters. Inspired by the neuroscience observation that different neuronal mechanisms exist in disparate brain regions, we propose a new spiking neuronal mechanism, named learnable thresholding, to address this issue. Utilizing learnable threshold values, learnable thresholding enables flexible neuronal mechanisms across layers, proper information flow within the network, and fast network convergence. In addition, we propose a moderate dropout method to serve as an enhancement technique to minimize inconsistencies between independent dropout runs. Finally, we evaluate the robustness of the proposed learnable thresholding and moderate dropout for image classification with different initial thresholds for various types of datasets. Our proposed methods produce superior results compared to other approaches for almost all datasets with fewer timesteps. Our codes are available at https://github.com/sq117/LTMD.git.

Indoor positioning is a key technology that enables the development of various smart and autonomous indoor applications. In recent years, wireless device is considered a promising technology enabler for indoor positioning despite facing various problems in the form of spatial, temporal, and device variation. Here we present a survey of problems in wireless-based indoor positioning and the state-of-the-art approaches aimed to solving those problems. This survey paper discusses various positioning metrics, parametric and non-parametric models to deal with spatial and temporal variation, as well as various methods that address device heterogeneity. In addition, some interesting fusion approaches are discussed. This paper aims to help researchers understand the current state of progress in wireless-based indoor positioning and identify areas that still need more research and improvement.

In recent decades, a plethora of dedicated evolutionary algorithms (EAs) have been crafted to solve domain-specific complex problems more efficiently. Many advanced EAs have relied on the incorporation of domain-specific knowledge as inductive biases that is deemed to fit the problem of interest well. As such, the embedment of domain knowledge about the underlying problem within the search algorithms is becoming an established mode of enhancing evolutionary search performance. In this paper, we present a study on evolutionary memetic computing paradigm that is capable of learning and evolving knowledge meme that traverses different but related problem domains, for greater search efficiency. Focusing on combinatorial optimization as the area of study, a realization of the proposed approach is investigated on two NP-hard problem domains (i.e., capacitated vehicle routing problem and capacitated arc routing problem). Empirical studies on well-established routing problems and their respective state-of-the-art optimization solvers are presented to study the potential benefits of leveraging knowledge memes that are learned from different but related problem domains on future evolutionary search.

The seven full papers and one technical correspondence in this special issue focus on the engineering applications of memetic computing.

Memetic computation is a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving. It covers a plethora of potentially rich meme-inspired computing methodologies, frameworks and operational algorithms including simple hybrids, adaptive hybrids and memetic automaton. In this paper, a comprehensive multi-facet survey of recent research in memetic computation is presented.

A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperforms other methods both in the quality of solutions obtained and the number of function evaluations for a range of continuous optimization problems.

Memetic algorithms (MAs) represent one of the recent growing areas in evolutionary algorithm (EA) research. The term MAs is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian EAs, Lamarckian EAs, cultural algorithms, or genetic local searches. In the last decade, MAs have been demonstrated to converge to high-quality solutions more efficiently than their conventional counterparts on a wide range of real-world problems. Despite the success and surge in interests on MAs, many of the successful MAs reported have been crafted to suit problems in very specific domains. Given the restricted theoretical knowledge available in the field of MAs and the limited progress made on formal MA frameworks, we present a novel probabilistic memetic framework that models MAs as a process involving the decision of embracing the separate actions of evolution or individual learning and analyzing the probability of each process in locating the global optimum. Further, the framework balances evolution and individual learning by governing the learning intensity of each individual according to the theoretical upper bound derived while the search progresses. Theoretical and empirical studies on representative benchmark problems commonly used in the literature are presented to demonstrate the characteristics and efficacies of the probabilistic memetic framework. Further, comparisons to recent state-of-the-art evolutionary algorithms, memetic algorithms, and hybrid evolutionary-local search demonstrate that the proposed framework yields robust and improved search performance.

Many deterministic algorithms in the context of constrained optimization require the first-order derivatives, or the gradient vectors, of the objective and constraint functions to determine the next feasible direction along which the search should progress. Although the second-order derivatives, or the Hessian matrices, are also required by some methods such as the sequential quadratic programming (SQP), their values can be approximated based on the first-order information, making the gradients central to the deterministic algorithms for solving constrained optimization problems. In this paper, two ways of obtaining the gradients are compared under the framework of the simple memetic algorithm (MA) employing genetic algorithm (GA) and SQP. Despite the simplicity and straightforwardness of the finite-difference gradients, faster convergence rate can be achieved when the analytical gradients can be made available. The savings on the number of function evaluations as well as the amount of time taken to solve some benchmark problems are presented along with some discussions.

We report our work on the algorithmic development of an evolutionary methodology for automatic configuration of metaheuristic algorithms for solving complex combinatorial optimization problems.We term it Automatic Configuration Engine for Metaheuristics (ACEM). We first propose a novel Left Variation - Right Property (LVRP) tree structure to manage various metaheuristic procedures and properties. With LVRP tree, feasible configurations of metaheuristics can be easily specified. An evolutionary learning algorithm is then proposed to evolve the internal context of the trees based on pre-selected training set. Guided by a user-defined satisfaction function of the candidate algorithms, it converges to the optimal or a very good algorithm. The experimental comparison with two recent state-of-the-art algorithms for solving the quadratic assignment problem (QAP) shows that ACEM produces an hybrid-genetic algorithm with human-competitive or even better performance.

In recent years, there has been an increase in research activities on Memetic Algorithm (MA). MA works with memes; a meme being defined as "the basic unit of cultural transmission, or imitation" [5]. In this respect, a Memetic Algorithm essentially refers to "an algorithm that mimics the mechanisms of cultural evolution". To date, there has been significant effort in bringing MA closer to the idea of cultural evolution. In this paper we assess MAs from the perspectives of "Universal Darwinism" and "Memetics". Subsequently, we propose a Diffusion Memetic Algorithm where the memetic material is transmitted by means of non-genetic transfer. Numerical studies are presented based on some of the commonly used synthetic problems in continuous optimization.

Algorithm Development Environment for Permutation-based problems (ADEP) is a software environment for configuring meta-heuristics for solving combinatorial optimization problems. This paper describes the key features of ADEP and how the environment was used to generate a Memetic Algorithm (MA) solution for Hamiltonian Cycle Problems (HCP). The effectiveness of the MA algorithm is demonstrated through computer simulations and its performance is compared with backtracking and other heuristic techniques such as Simulated Annealing, Tabu Search, and Ant Colony Optimization.

Distributed computing environments offer vast amounts of computational power for use in parallel memetic algorithms. However, they consist of heterogeneous computing nodes, in terms of computational power, operating platform, network connectivity and latency. The behavior of parallel memetic algorithms in such environment is poorly understood: the vast majority of current parallel MAs assumes homogeneous environment. To deal with the heterogeneity of the computing resources, a hierarchical model PMA (hPMA-DLS) is proposed to provide the speed-up regardless of the heterogeneity in the distributed environment while preserving the standard behavior of the PMA. The empirical study on several large scale quadratic assignment problems (QAPs) shows that hPMA-DLS can enhance the efficiency of the island model PMA-DLS [22] search without deterioration in the solution quality.

In this paper, we propose the island model parallel memetic algorithm with diversity-based dynamic adaptive strategy (PMA-DLS) for controlling the local search frequency and demonstrate its utility in solving complex combinatorial optimization problems, in particular large-scale quadratic assignment problems (QAPs). The empirical results show that PMA-DLS converges to competitive solutions at significantly lower computational cost when compared to the canonical MA and PMA. Furthermore, compared to our previous work on PMA using static adaptation strategy, it is found that the diversity-based dynamic adaptation strategy displays better robustness in terms of solution quality across the class of QAP problems considered without requiring extra effort in selecting suitable parameters.

In recent decades, many meta-heuristics, including genetic algorithm (GA), ant colony optimization (ACO) and various local search (LS) procedures have been developed for solving a variety of NP-hard combinatorial optimization problems. Depending on the complexity of the optimization problem, a meta-heuristic method that may have proven to be successful in the past might not work as well. Hence it is becoming a common practice to hybridize meta-heuristics and local heuristics with the aim of improving the overall performance. In this paper, we propose a novel adaptive GA-ACO-LS hybrid algorithm for solving quadratic assignment problem (QAP). Empirical study on a diverse set of QAP benchmark problems shows that the proposed adaptive GA-ACO-LS converges to good solutions efficiently. The results obtained were compared to the recent state-of-the-art algorithm for QAP, and our algorithm showed obvious improvement.

The problem of terrain modeling is basically a type of function approximation problem. This type of problem has been widely studied in the soft computing community. In recent years, neural networks have been successfully applied to surface reconstruction and classification problems involving scattered data. However, due to the iterative nature of training a neural network, the resulting high cost in computational time limits the implementation of machine learning based methods in many real world applications (for example, navigation applications in unmanned aerial vehicles) that require fast generation of terrain models. A recently proposed machine learning method, the extreme learning machine (ELM), is able to train single-layer feed forward neural networks with excellent speed and good generalization. In this paper, we present terrain modeling using various machine learning methods, and we compare the performances of these methods with ELM. We also present a comparison of terrain modeling performances between ELM and the popular choice of terrain and surface modeling technique, the Delaunay triangulation with linear interpolation. Our results show that machine learning using ELM offers a potential solution to terrain modeling problems with good performances

Terrain models that permit multiresolution access are essential for model predictive control of unmanned aerial vehicles in low-level flights. The authors present the extreme learning machine (ELM), a recently proposed learning paradigm, as a mechanism for learning the stored digital elevation information to allow multiresolution access. We give results of simulations designed to compare the performance of our approach with two other approaches for multiresolution access, namely: 1) linear interpolation on Delaunay triangles of the sampled terrain data points and 2) terrain learning using support vector machines (SVMs). The results show that to achieve the same mean square error during access, the memory needed in our approach is significantly lower. Additionally, the offline training time for the ELM network is much less than that for the SVM.

This paper describes a novel design of a fuzzy inference chip that allows for real-time online context switching. A context refers to a situation or scenario of an application requiring specific domain knowledge. In particular, our focus is on the class of applications involving embedded fuzzy control. The domain knowledge therefore refers to fuzzy rules and memberships. The kind of applications being considered is real-time in nature, which necessitates the implementation of hardware for fuzzy inferencing. The chip architecture is described and details on the design of the chip is presented.

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

Memetic algorithms have become to gain increasingly important for solving large scale combinatorial optimization problems. Typically, the extent of the application of local searches in canonical memetic algorithm is based on the principle of "more is better". In the same spirit, the island model parallel memetic algorithm (PMA) is an important extension of the canonical memetic algorithm which applies local searches to every transitional solutions being considered. For PMA which applies complete local search, we termed it as PMA-CLS. In this paper, we consider the island model PMA with selective application of local search (PMA-SLS) and demonstrate its utility in solving complex combinatorial optimization problems, in particular large-scale quadratic assignment problems (QAPs). Based on our empirical results, the PMA-SLS compared to the PMA-CLS, can reduce the computational time spent significantly with little or no lost of solution quality. This we concluded is due mainly to the ability of the PMA-SLS to manage a more desirable diversity profile as the search progresses.

Our goal is to decompose an arbitrary pathwise continuous rectilinear polygon with holes into a fixed number of nonoverlapping pathwise continuous rectilinear open subsets. This goal involves two additional hard constraints – a. The area of each subset must equal an a priori value. b. The union of the closure of the subsets must be equal to the polygon itself. A soft constraint is that any edge that defines the closure of any subset measures ω units or more. We give a decomposition algorithm that satisfies these constraints. Our goal is motivated by a problem involving parallel utilization of multiple unmanned reconnaissance aerial vehicles (URAVs) with heterogeneous endurance characteristics and equipped with similar sensors with heterogeneous capabilities to conduct a photographic scan of regions with urban-like structures. We discuss our motivation in greater details.

An unmanned reconnaissance aerial vehicle mounted sensor of footprint with a small area /spl omega//sub s//sup 2/ is used to cover critical airbase structures for damage assessment. The region of coverage interest is modeled with a closed union of a minimal set of interior disjoint rectangles of width /spl omega//sub i/ = /spl omega//sub s/. We wish to find a minimum-length flight path for complete coverage for the nonholonomic vehicle. We prove that our minimization problem is a traveling salesman problem (TSP) that is symmetric, non-Euclidean and satisfies the triangular inequality. We compare our modeling primitive with two simple primitives commonly used for representing coverage regions. An ant colony optimization heuristic for solving the TSP is presented.

This paper extends our previous work on the island model parallel hybrid-genetic algorithm (PHGA) for large scale quadratic assignment problems (QAPs). Some issues on the control parameters of the migration process and how they affect the quality of the solutions and the efficiency of algorithm deserve further evaluative study. In this paper, we investigate the effect of migration topology on the performance of the PHGA. Two topologies, one-way ring topology and random topology, are studied and analyzed. The empirical results show that the PHGA with ring topology is better able to achieve an appropriate tradeoff between exploration and exploitation and hence more helpful to improve the performance of PHGA for solving large scale QAPs.

In this paper, we consider a parallel hybrid-GA (PHGA) for solving large combinatorial optimization problem. The approach of our PHGA is based on the island model whereby islands of subpopulations are farmed to multiple processing nodes for execution. The PHGA was applied to solve the quadratic assignment problems (QAP) to demonstrate the potential effectiveness of the models. In particular, we concentrate on QAP benchmarks of high complexity for n ranging from 60 to 256. Our results show that a two-island PHGA which employs a simplistic elite migration between islands outperforms the serial hybrid-GA (SHGA) significantly. As the size and complexity of the problem increases, the advantage of the PHGA in terms of computation time and solution quality becomes more evident. This opens up a wide channel for further exploration on implementation of the PHGA in a grid computing environment.

This paper describes an automated curriculum timetabling system based on a stochastic search methodology, namely a co-evolutionary algorithm. The application timetable is taken from the undergraduate courses of the School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU). A co-evolutionary algorithm approach is found to be well suited. Practical courses have duration greater than one hour. A schedule can be generated separately and its population, which consists of a set of practical schedules, is termed as the practical population. Lecture and tutorial schedules can also be generated separately. These are of one-hour duration and they are termed collectively as lecture/tutorial schedule. A set of lecture/tutorial schedules could be generated to form the lecture/tutorial population. These two populations use the same set of resources and have constraining effects upon one another. Since the placement of practical courses have a more constraining effect, the schedules in the practical population are first generated and are then used to guide the generation of the set of lecture/tutorial schedules. For every lecture/tutorial schedule generated, it is combined with its corresponding practical schedule to form a combined schedule. The average fitness of all the combined schedules is then computed and used as a measure of the fitness of the practical schedule that drives them. The practical population is then evolved progressively to obtain the best practical schedule. It is then used as a base configuration for the rest of the courses to populate and evolve. The resultant system compares favorably to the current manual system.

We present an approach to compute the efficient frontier for portfolio optimization based on evolutionary programming (EP) technique. Our approach relies on multiple EP runs within a search to create the frontier. Results from simulation, which runs on a personal computer platform, are shown for data set consisting of 24 types of securities. The algorithm converges quickly with consistent performance, making it suitable for creating an efficient frontier for a much larger number of assets. The versatility of the approach makes it viable to accommodate constraints or scenarios, which we perceive as either investors or market imposed conditions. Our technique opens up an avenue to conveniently overcome the symptomatic "unrealizable or unreasonable portfolios" syndrome that plagued methodology that relies on identifying corner portfolios as a basis for creating the frontier.

Chartists usually rely on technical indicators to predict trends in time series charts. Although the indicators are precise and most practitioners tend to concur to a large extent on the general meaning of the indicators, it is hard to specify precise thresholds as a basis for deciding on a particular course of action. Probabilistic based approaches do offer recourse for handling such kinds of uncertainty. However, a probabilistic mode of managing uncertainty lacks the flair for capturing the essence of subjectivity that unfortunately (or fortunately) is an inherent trait of human chartists. Fuzzy logic therefore offers a better alternative in this sense. It is further suggested that the fuzzy knowledge can be tuned by means of various existing learning algorithms to accommodate the needs of the chartists.