Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

Generalized disjunction decomposition for evolvable hardware

Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the “generalized disjunction decomposition” (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using the$(1 + lambda)$evolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided

Genetic Programming + Unfolding Embryology in Automated Layout Planning

Automated layout planning aims to the implementation of computational methods for the generation and the optimization of floor plans, considering the spatial configuration and the assignment of activities. Sophisticated strategies such as Genetic Algorithms have been implemented as heuristics of good solutions. However, the generative forces that derive from the social structures have been often neglected. This research aims to illustrate that the data that encode the layout’s social and cultural generative forces, can be implemented within an evolutionary system for the design of residential layouts. For that purpose a co-operative system was created, which is composed of a Genetic Programming algorithm and an agent-based unfolding embryology procedure that assigns activities to the spaces generated by the GP algorithm. The assignment of activities is a recursive process which follows instructions encoded as permeability graphs. Furthermore, the Ranking Sum Fitness evaluation method is proposed and applied for the achievement of multi-objective optimization. Its efficiency is tested against the Weighted-Sum Fitness function. The system’s results, both numerical and spatial, are compared to the results of a conventional evolutionary approach. This comparison showed that, in general, the proposed system can yield better solutions

Plot-based urbanism and urban morphometrics : measuring the evolution of blocks, street fronts and plots in cities

Generative urban design has been always conceived as a creation-centered process, i.e. a process mainly concerned with the creation phase of a spatial transformation. We argue that, though the way we create a space is important, how that space evolves in time is ways more important when it comes to providing livable places gifted by identity and sense of attachment. We are presenting in this paper this idea and its major consequences for urban design under the title of “Plot-Based Urbanism”. We will argue that however, in order for a place to be adaptable in time, the right structure must be provided “by design” from the outset. We conceive urban design as the activity aimed at designing that structure. The force that shapes (has always shaped) the adaptability in time of livable urban places is the restless activity of ordinary people doing their own ordinary business, a kind of participation to the common good, which has hardly been acknowledged as such, that we term “informal participation”. Investigating what spatial components belong to the spatial structure and how they relate to each other is of crucial importance for urban design and that is the scope of our research. In this paper a methodology to represent and measure form-related properties of streets, blocks, plots and buildings in cities is presented. Several dozens of urban blocks of different historic formation in Milan (IT) and Glasgow (UK) are surveyed and analyzed. Effort is posed to identify those spatial properties that are shared by clusters of cases in history and therefore constitute the set of spatial relationships that determine the morphological identity of places. To do so, we investigate the analogy that links the evolution of urban form as a cultural construct to that of living organisms, outlining a conceptual framework of reference for the further investigation of “the DNA of places”. In this sense, we identify in the year 1950 the nominal watershed that marks the first “speciation” in urban history and we find that factors of location/centrality, scale and street permeability are the main drivers of that transition towards the entirely new urban forms of contemporary cities

Optimal advertising campaign generation for multiple brands using MOGA

The paper proposes a new modified multiobjective genetic algorithm (MOGA) for the problem of optimal television (TV) advertising campaign generation for multiple brands. This NP-hard combinatorial optimization problem with numerous constraints is one of the key issues for an advertising agency when producing the optimal TV mediaplan. The classical approach to the solution of this problem is the greedy heuristic, which relies on the strength of the preceding commercial breaks when selecting the next break to add to the campaign. While the greedy heuristic is capable of generating only a group of solutions that are closely related in the objective space, the proposed modified MOGA produces a Pareto-optimal set of chromosomes that: 1) outperform the greedy heuristic and 2) let the mediaplanner choose from a variety of uniformly distributed tradeoff solutions. To achieve these results, the special problem-specific solution encoding, genetic operators, and original local optimization routine were developed for the algorithm. These techniques allow the algorithm to manipulate with only feasible individuals, thus, significantly improving its performance that is complicated by the problem constraints. The efficiency of the developed optimization method is verified using the real data sets from the Canadian advertising industry

Modelling of integrated vehicle scheduling and container storage problems in unloading process at an automated container terminal

Effectively scheduling vehicles and allocating storage locations for containers are two important problems in container terminal operations. Early research efforts, however, are devoted to study them separately. This paper investigates the integration of the two problems focusing on the unloading process in an automated container terminal, where all or part of the equipment are built in automation. We formulate the integrated problem as a mixed-integer programming (MIP) model to minimise ship’s berth time. We determine the detailed schedules for all vehicles to be used during the unloading process and the storage location to be assigned for all containers. A series of experiments are carried out for small-sized problems by using commercial software. A genetic algorithm (GA) is designed for solving large-sized problems. The solutions from the GA for the small-sized problems are compared with the optimal solutions obtained from the commercial software to verify the effectiveness of the GA. The computational results show that the model and solution methods proposed in this paper are efficient in solving the integrated unloading problem for the automated container terminal

Shaper-GA: automatic shape generation for modular housing

This work presents an automatic system that, from the specification of an architectural language of design, generates several alternative floor plants for the construction of modular homes. The system uses Genetic Algorithms and is capable of efficiently producing various plant solutions. The rules of architecture are implemented in the fitness function translating the rules of a Shape Grammar created by the architect. Different solutions of feasible plants are generated, that is, solutions that obey the rules of Shape Grammar and do not have overlays between the rooms. The system can be integrated with a user-friendly interface in the future, to allow for the house owners customization of their own house. Such a tool can also be delivered to construction companies for them to manage the design of modular houses that meet specific clients requirements.Este trabalho apresenta um sistema automático que, a partir da especificação de uma linguagem arquitetural de design, gera plantas alternativas para residências de construção modular. O sistema usa Algoritmos Genéticos e é capaz de produzir várias soluções de plantas de modo eficiente. As regras de arquitetura são implementadas na função de fitness a partir de uma Gramática de Forma criada pelo arquiteto. São geradas diferentes soluções de plantas exequíveis, isto é, soluções que obedecem à Gramática de Forma e não têm sobreposições entre as suas divisões. Pode ser futuramente integrado com uma interface amigável para o utilizador de forma a que este personalize e crie a sua futura casa. Tal ferramenta pode também ser entregue às companhias de construção de forma a que estas gerem uma planta para uma casa modular personalizada