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Evolutionary gradient search is a hybrid algorithm that exploits the complementary features of gradient search and evolutionary algorithm to achieve a level of efficiency and robustness that cannot be attained by either techniques alone. Unlike the conventional coupling of local search operators and evolutionary algorithm, this algorithm follows a trajectory based on the gradient information that is obtain via the evolutionary process. In this paper, we consider how gradient information can be obtained and used in the context of multi-objective optimization problems. The different types of gradient information are used to guide the evolutionary gradient search to solve multi-objective problems. Experimental studies are conducted to analyze and compare the effectiveness of various implementations.

The additive recurrent network structure of linear threshold neurons represents a class of biologically-motivated models, where nonsaturating transfer functions are necessary for representing neuronal activities, such as that of cortical neurons. This paper extends the existing results of dynamics analysis of such linear threshold networks by establishing new and milder conditions for boundedness and asymptotical stability, while allowing for multistability. As a condition for asymptotical stability, it is found that boundedness does not require a deterministic matrix to be symmetric or possess positive off-diagonal entries. The conditions put forward an explicit way to design and analyze such networks. Based on the established theory, an alternate approach to study such networks is through permitted and forbidden sets. An application of the linear threshold (LT) network is analog associative memory, for which a simple design method describing the associative memory is suggested in this paper. The proposed design method is similar to a generalized Hebbian approach, but with distinctions of additional network parameters for normalization, excitation and inhibition, both on a global and local scale. The computational abilities of the network are dependent on its nonlinear dynamics, which in turn is reliant upon the sparsity of the memory vectors.

Abstract-This paper considers the assignment of tasks with interdependencies in a heterogeneous multiprocessor environment where task execution time varies with task as well as the processing element processing it. The solution to this heterogeneous multiprocessor scheduling problem involves the optimization of complete task assignments and processing order within the assigned processors with minimum makespan, subject to the precedence constraint. To solve such a NP-hard combinatorial optimization problem, this paper presents a hybrid evolutionary algorithm that incorporates two local search heuristics that exploits the intrinsic structure of the solution as well as specialized genetic operators to encourage exploration of the search space. The effectiveness and contribution of the proposed features are validated on a set of benchmark problems characterized by different degrees of communication times, task and processor heterogeneities. Simulation results demonstrate the algorithm is capable of finding useful schedules on the set of new benchmark problems.Index Terms-Multiprocessor scheduling, heterogeneous, hybrid evolutionary algorithm, local search, precedence