E. Di Paolo

Binary-Representation-Based Genetic Algorithm for Aircraft Arrival Sequencing and Scheduling

Arrival sequencing and scheduling (ASS) at airports is an NP-hard problem. Much effort has been made to use permutation-representation-based genetic algorithms (GAs) to tackle this problem, whereas this paper attempts to design an efficient GA based on a binary representation of arriving queues. Rather than using the order and/or arriving time of each aircraft in the queue to construct chromosomes for GAs, this paper uses the neighboring relationship between each pair of aircraft, and the resulted chromosome is a 0-1-valued matrix. A big advantage of this binary representation is a highly efficient uniform crossover operator, which is normally not applicable to those permutation representations. The strategy of receding horizon control (RHC) is also integrated into the new GA to attack the dynamic ASS problem. An extensive comparative simulation study shows that the binary-representation-based GA outperforms the permutation-representation-based GA.

Evolving neural models of path integration

SUMMARY We use a genetic algorithm to evolve neural models of path integration, with particular emphasis on reproducing the homing behaviour of Cataglyphis fortis ants. This is done within the context of a complete model system, including an explicit representation of the animals movements within its environment. We show that it is possible to produce a neural network without imposing a priori any particular system for the internal representation of the animals home vector. The best evolved network obtained is analysed in detail and is found to resemble the bicomponent model of Mittelstaedt. Because of the presence of leaky integration, the model can reproduce the systematic navigation errors found in desert ants. The model also naturally mimics the searching behaviour that ants perform once they have reached their estimate of the nest location. The results support possible roles for leaky integration and cosine-shaped compass response functions in path integration.

An efficient Genetic Algorithm with uniform crossover for the multi-objective Airport Gate Assignment Problem

Genetic Algorithms (GAs) have a good potential of solving the Gate Assignment Problem (GAP) at airport terminals, and the design of feasible and efficient evolutionary operators, particularly, the crossover operator, is crucial to successful implementations. This paper reports an application of GAs to the multi-objective GAP. The relative positions between aircraft rather than their absolute positions in the queues to gates is used to construct chromosomes in a novel encoding scheme, and a new uniform crossover operator, free of feasibility problems, is then proposed, which is effective and efficient to identify, inherit and protect useful common sub-queues to gates during evolution. Extensive simulation studies illustrate the advantages of the proposed GA scheme with uniform crossover operator.

Environmental regulation can arise under minimal assumptions.

Models that demonstrate environmental regulation as a consequence of organism and environment coupling all require a number of core assumptions. Many previous models, such as Daisyworld, require that certain environment-altering traits have a selective advantage when those traits also contribute towards global regulation. We present a model that results in the regulation of a global environmental resource through niche construction without employing this and other common assumptions. There is no predetermined environmental optimum towards which regulation should proceed assumed or coded into the model. Nevertheless, polymorphic stable states that resist perturbation emerge from the simulated co-evolution of organisms and environment. In any single simulation a series of different stable states are realised, punctuated by rapid transitions. Regulation is achieved through two main subpopulations that are adapted to slightly different resource values, which force the environmental resource in opposing directions. This maintains the resource within a comparatively narrow band over a wide range of external perturbations. Population driven oscillations in the resource appear to be instrumental in protecting the regulation against mutations that would otherwise destroy it. Sensitivity analysis shows that the regulation is robust to mutation and to a wide range of parameter settings. Given the minimal assumptions employed, the results could reveal a mechanism capable of environmental regulation through the by-products of organisms.

Calculating Complete and Exact Pareto Front for Multiobjective Optimization: A New Deterministic Approach for Discrete Problems

Searching the Pareto front for multiobjective optimization problems usually involves the use of a population-based search algorithm or of a deterministic method with a set of different single aggregate objective functions. The results are, in fact, only approximations of the real Pareto front. In this paper, we propose a new deterministic approach capable of fully determining the real Pareto front for those discrete problems for which it is possible to construct optimization algorithms to find the k best solutions to each of the single-objective problems. To this end, two theoretical conditions are given to guarantee the finding of the actual Pareto front rather than its approximation. Then, a general methodology for designing a deterministic search procedure is proposed. A case study is conducted, where by following the general methodology, a ripple-spreading algorithm is designed to calculate the complete exact Pareto front for multiobjective route optimization. When compared with traditional Pareto front search methods, the obvious advantage of the proposed approach is its unique capability of finding the complete Pareto front. This is illustrated by the simulation results in terms of both solution quality and computational efficiency.

Spatial embedding and the structure of complex networks

We review and discuss the structural consequences of embedding a random network within a metric space such that nodes distributed in this space tend to be connected to those nearby. We find that where the spatial distribution of nodes is maximally symmetrical some of the structural properties of the resulting networks are similar to those of random nonspatial networks. However, where the distribution of nodes is inhomogeneous in some way, this ceases to be the case, with consequences for the distribution of neighborhood sizes within the network, the correlation between the number of neighbors of connected nodes, and the way in which the largest connected component of the network grows as the density of edges is increased. We present an overview of these findings in an attempt to convey the ramifications of spatial embedding to those studying real-world complex systems.

Ripple-spreading model and Genetic Algorithm for random complex networks: Preliminary study

Recently complex network theory has been broadly applied in various domains. How to effectively and efficiently optimize the topology of complex networks remains largely an unsolved fundamental question. When applied to the network topology optimization, Genetic Algorithms (GAs) are often confronted with permutation representation, memory-inefficiency and stochastic modeling problems, as well as difficulties in the design of problem-specific evolutionary operators. This paper, inspired by the natural ripple spreading phenomenon, reports a deterministic model of random complex networks. Unlike existing stochastic models, the topology of a random network can be thoroughly determined by some ripple-spreading related parameters in the new model. Therefore, the network topology can be improved by optimize these ripple-spreading related parameters. As a result, no purpose-designed GA is required, but a very basic binary GA, compatible to all classic evolutionary operators, can be applied in a straightforward way. Preliminary simulation results demonstrate the potential of the proposed ripple-spreading model and GA for the topology optimization of random complex networks.

An evolved agent performing efficient path integration based homing and search

This paper presents analysis and follow up experiments based on previous work where a neurally controlled simulated agent was evolved to navigate using path integration (PI). Specifically, we focus on one agent, the best one produced, and investigate two interesting features. Firstly, the agent stores its current coordinates in two leaky integrators, whose leakage is partially compensated for by a normalisation mechanism. We use a comparison between four network topologies to test if this normalised leakage mechanism is adaptive for the agent. Secondly, the controller generates efficient searching behaviour in the vicinity of its final goal. We begin an analysis of the dynamical system (DS) responsible for this, starting from a simple three variable system.