###### Abstract
In such cases, it would be impossible to make a decision based on simply observing the given data, because the data points are likely to be as diverse as they are similar, based on the given objective and predefined parameters. The optimization process involves the development of a mathematical model of the problem, which represents the problem's variables, constraints, and features [16]. This mathematical model of the problem being optimized is commonly referred to as the objective function (OF). When a suitable OF has been obtained, the next step involves selecting a suitable optimization algorithm to find the best possible extreme trade-off among the variables (and, in the case of multiple objectives, among the objectives) that best solves the problem. Popular search strategies include stochastic, deterministic, and evolutionary algorithms [16]. The fundamental concepts of evolutionary algorithms (EAs) are inspired by two biological phenomena [12; 17]: (1) the characteristics of living beings are encoded (represented) using genetic information; and (2) evolution is the result of the interaction between the random creation of new genetic information and the selection of those individuals that are best adapted to the ecosystem [5]. In an EA, candidate solutions ('individuals') for the OF are encoded in a way that simplifies the search for the optimal solution. A set of individuals ('population') is evolved by applying operators (mutation, crossover, selection) in each iteration ('generation'). When fulfilling a stop criterion, the EA ends in providing the solution that optimizes the OF (the individual that is best adapted to the ecosystem, in the biological analogy). More details of EAs applied to DER can be found in [PERSON] et al. [5]. With respect to evolutionary algorithms, they are classified as single-objective, multi-objective, or many-objective. The advantage of multi-objective evolutionary algorithms is that they find a set of non-dominated solutions rather than a single optimal solution. The process of optimizing a combination of alternative energy sources with respect to cost minimization and efficient energy supply from the demand side involves developing an OF representing the various sources in the DER mix, subject to specified constraints. The result of the optimization process is a combination of DER technologies that could benefit the consumer from both a cost and demand perspective. This ensures that both sides of the distribution network benefit mutually. A typical optimization process is shown in Figure 1. The process of parameter selection and optimization has been investigated by [PERSON] & [PERSON] and [PERSON] in [PERSON] _et al._ (2012); [PERSON] _et al._ (2012). Reinforcement learning has been applied to the training and validation of network parameters for wireless communications systems. This paper adopts a similar approach, and involves the optimization of a cost function for selected combinations of DER technologies in a mid-rise apartment building. Using a low-level Pareto-based evolutionary algorithm, the optimal mix of energy sources is obtained for four scenarios: a base case with no DER, and three other cases involving varied combinations of PV solar panel, battery storage, air source heat pump, and load shifting. The rest of the paper is organized as follows: Section 2 discusses the impact of optimization in the determination of DER dispatch strategies. Section 3 discusses the DER-CAM and Pareto-based cost functions for the four selected cases of DER strategies, as well as the parameter specification for the two-objective cost function. Section 4 discusses the outcome of the optimization using the multi-objective evolutionary algorithm, and compares the results with DER-CAM optimization. Section 5 concludes the paper.