In this paper we examine feed-forward neural networks using genetic algorithms in the training process instead of error backpropagation algorithm. Additionally real encoding is preferred to binary encoding as it is more appropriate to find the optimum weights. We use learning and momentum rates...

We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The "curse of dimensionality" is avoided due to four complementary techniques: (1) exploiting...

This paper proposes a novel method of global optimization based on host-parasite co-evolution. It also develops a Fortran-77 code for the algorithm. The algorithm has been tested on 100 benchmark functions (of which the results of 32 relatively harder problems have been reported). In its search...

This note is intended to demonstrate that median (a well-known measure of central tendency) is a weighted arithmetic mean of all sample observations and such (non-trivial) weights may be computed by an optimization method such as the host-parasite co-evolutionary algorithm

This thesis investigates models of market risk assessment based on genetic algorithms, with specific reference to asset portfolio choice under volatile market conditions. It does so by developing computational simulations of asset portfolios, which are then subjected to stressful price events. A...

This paper discusses the role that Genetic Algorithms (GA) can have in determining asset allocation for multi sector funds. We present an asset allocation model where the investors' utility function departs from the quadratic utility function assumed by the standard Mean-Variance optimisation....

This paper presents a new numerical method for solving stochastic general equilibrium models with dynamic portfolio choice over many financial assets. The method can be applied to models where there are heterogeneous agents, time-varying investment opportunity sets, and incomplete asset markets....