We consider the problem of online audio source separation. Existing algorithms adopt either a sliding block approach or a stochastic gradient approach, which is faster but less accurate. Also, they rely either on spatial cues or on spectral cues and cannot separate certain mixtures. In this paper, we design a general online audio source separation framework that combines both approaches and both types of cues. The model parameters are estimated in the Maximum Likelihood (ML) sense using a Generalised Expectation Maximisation (GEM) algorithm with multiplicative updates. The separation performance is evaluated as a function of the block size and the step size and compared to that of an offline algorithm.

"The task of the 11-bit boolean multiplexer is to decode a 3-bit binary address (000, 001, 010, 011, 100, 101, 110, 111) and return the value of the corresponding data register (d0, d1, d2, d3, d4, d5, d6, d7). Thus, the boolean 11-multiplexer is a function of 11 arguments: three, a0 to a2, determine the address, and eight, d0 to d7, determine the answer. As GEP uses single-character chromosomes, T = {a, b, c, 1, 2, 3, 4, 5, 6, 7, 8} which correspond, respectively, to {a0, a1, a2, d0, d1, d2, d3, d4, d5, d6, d7}."

"We consider the problem of parameter estimation for a system of ordinary differential equations from noisy observations on a solution of the system. In case the system is nonlinear, as it typically is in practical applications, an analytic solution to it usually does not exist. Consequently, straightforward estimation methods like the ordinary least squares method depend on repetitive use of numerical integration in order to determine the solution of the system for each of the parameter values considered, and to find subsequently the parameter estimate that minimises the objective function. This induces a huge computational load to such estimation methods. We propose an estimator that is defined as a minimiser of an appropriate distance between a nonparametrically estimated derivative of the solution and the right-hand side of the system applied to a nonparametrically estimated solution.…"

"… The aim is to find efficient decompositions that simultaneously minimize the irradiation time, the cardinality of the decomposition and the setup-time to configure the multi-leaf collimator at each step of the decomposition. We propose for this NP-hard multiobjective combinatorial problem a heuristic, based on the adaptation of the two-phase Pareto local search. Experiments are carried out on different size instances and the results are reported."

"The knapsack problem (KP) and its multidimensional version (MKP) are basic problems in combinatorial optimization. In this paper we consider their multiobjective extension (MOKP and MOMKP), for which the aim is to obtain or to approximate the set of efficient solutions. In a first step, we classify and describe briefly the existing works, that are essentially based on the use of metaheuristics. In a second step, we propose the adaptation of the two-phase Pareto local search (2PPLS) to the resolution of the MOMKP. With this aim, we use a very-large scale neighborhood (VLSN) in the second phase of the method, that is the Pareto local search. We compare our results to state-of-the-art results and we show that we obtain results never reached before by heuristics, for the biobjective instances. Finally we consider the extension to three-objective instances."

"We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x = x^\star. The objective function of the corresponding optimization problem is the sum of private (known only by a node,) convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, decentralized algorithm."

"The Dendritic Cell Algorithm (DCA) is inspired by the function of the dendritic cells of the human immune system. In nature, dendritic cells are the intrusion detection agents of the human body, policing the tissue and organs for potential invaders in the form of pathogens. In this research, and abstract model of DC behaviour is developed and subsequently used to form an algorithm, the DCA. The abstraction process was facilitated through close collaboration with laboratory- based immunologists, who performed bespoke experiments, the results of which are used as an integral part of this algorithm. The DCA is a population based algorithm, with each agent in the system represented as an 'artificial DC'. Each DC has the ability to combine multiple data streams and can add context to data suspected as anomalous.…"

"The Lin-Kernighan heuristic is known to be one of the most successful heuristics for the Traveling Salesman Problem (TSP). It has also proven its efficiency in application to some other problems. In this paper we discuss possible adaptations of TSP heuristics for the Generalized Traveling Salesman Problem (GTSP) and focus on the case of the Lin-Kernighan algorithm. At first, we provide an easy-to-understand description of the original Lin-Kernighan heuristic. Then we propose several adaptations, both trivial and complicated. Finally, we conduct a fair competition between all the variations of the Lin-Kernighan adaptation and some other GTSP heuristics. It appears that our adaptation of the Lin-Kernighan algorithm for the GTSP reproduces the success of the original heuristic. Different variations of our adaptation outperform all other heuristics in a wide range of trade-offs between solution quality and running time, making Lin-Kernighan the state-of-the-art GTSP local search."

"The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics.…"

"In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm Optimizers (PSO) are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization problems. Dependence of the performances in the conditioning of the problem and rotational invariance of the algorithms are in particular investigated."

"In this paper we present a novel genetic algorithm (GA) solution to a simple yet challenging commercial puzzle game known as the Zen Puzzle Garden (ZPG). We describe the game in detail, before presenting a suitable encoding scheme and fitness function for candidate solutions. We then compare the performance of the genetic algorithm with that of the A* algorithm. Our results show that the GA is competitive with informed search in terms of solution quality, and significantly out-performs it in terms of computational resource requirements. We conclude with a brief discussion of the implications of our findings for game solving and other "real world" problems."

"The population annealing algorithm introduced by Hukushima and Iba is described. Population annealing combines simulated annealing and Boltzmann weighted differential reproduction within a population of replicas to sample equilibrium states. Population annealing gives direct access to the free energy. It is shown that unbiased measurements of observables can be obtained by weighted averages over many runs with weight factors related to the free energy estimate from the run. Population annealing is well suited to parallelization and may be a useful alternative to parallel tempering for systems with rough free energy landscapes such as spin glasses. The method is demonstrated for spin glasses."

"This article presents a generic method to solve 2D multi-objective placement problem for free-form components. The proposed method is a relaxed placement technique combined with an hybrid algorithm based on a genetic algorithm and a separation algorithm. The genetic algorithm is used as a global optimizer and is in charge of efficiently exploring the search space. The separation algorithm is used to legalize solutions proposed by the global optimizer, so that placement constraints are satisfied. A test case illustrates the application of the proposed method. Extensions for solving the 3D problem are given at the end of the article."

"Central Force Optimization is a global search and optimization algorithm that searches a decision space be flying "probes" whose trajectories are deterministically computed using two equations of motion. Because it is possible for a probe to fly outside the domain of feasible solutions, a simple errant probe retrieval method has been used previously that does not include the directional information contained in a probe's acceleration vector. This note investigates the effect of adding directionality to the "repositioning factor" approach. As a general proposition, it appears that doing so does not improve convergence speed or accuracy. In fact, adding directionality to the original errant probe retrieval scheme appears to be highly inadvisable. Nevertheless, there may be alternative probe retrieval schemes that do benefit from directional information, and the results reported here may assist in or encourage their development."

"Complexity of global optimization algorithms makes implementation of the algorithms difficult and leads the algorithms to require more computer resources for the optimization process. The ability to explore the whole solution space without increasing the complexity of algorithms has a great importance to not only get reliable results but so also make the implementation of these algorithms more convenient for higher dimensional and complex-real world problems in science and engineering. In this paper, we present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique that is designed into a group architecture similar to the architecture of Cooperative Coevolutionary Algorithms.…"

"…The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser. We will discuss the unique search features used in CS and the implications for fur- ther research."

"Pay attention to the path the steepest descent takes until finding the optimum at x = 1 and y = 1. It is said that an evolution strategy would follow a similar path if its population could be infinite. Since, so far, there are no real computers with infinite memory, such assumption cannot be verified in the real world."

"Parallel Global Optimization Algorithms (PGOA) provide an efficient way of dealing with hard optimization problems. One method of parallelization of GOAs that is frequently applied and commonly found in the contemporary literature is the so-called Island Model (IM). In this paper we analyze the impact of the migration topology on the performance of a PGOA which uses the Island Model. In particular we consider parallel Differential Evolution and Simulated Annealing with Adaptive Neighborhood and draw first conclusions that emerge from the conducted experiments."

"This article describes the R package DEoptim which implements the differential evolution algorithm for the global optimization of a real-valued function of a real-valued parameter vector. The implementation of differential evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated via case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-Switching Generalized AutoRegressive Conditional Heteroskedasticity (MSGARCH) model for the returns of the Swiss Market Index."

Nonplussed (but satisfying) to see somebody do such a thorough job discussing something I was planning on doing almost 20 years ago…

Grammatical Evolution Ruby Exploratory Toolkit

"About the Book: This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts. It was developed as a series of lecture notes for an undergraduate course I taught at GMU. The chapters are designed to be printable separately if necessary. As it's lecture notes, the topics are short and light on examples and theory. It's best when complementing other texts. With time, I might remedy this."

Me: "Solving interesting problems" well involves a frequent iterative dialog between the techniques, intermediate results, and the practitioner. Agility, in other words.

"PSwarm is a global optimization solver for bound and linear constrained problems (for which the derivatives of the objective function are unavailable, inaccurate or expensive).

The algorithm combines pattern search and particle swarm. Basically, it applies a directional direct search in the poll step (coordinate search in the pure simple bounds case) and particle swarm in the search step."

Proof-of-concept of distributed population-based metaheuristics running on a number of phones.

Every blasted conference in the field is the same time of year. And all the US conferences are in the south or the desert, in July. Wonder what the total air conditioning bills are for all of them together?