## 1 Introduction  
Time series currently have multiple applications in fields such as medicine and the financial market. One of the most important financial markets is the Foreign Exchange (FOREX). International trade sharing and exchange are conducted at FOREX. In other words, FOREX is a financial market in which the values of currencies are traded. Initially, this exchange was due to the sale of goods and services. However, today, these operations represent a tiny percentage of its activity, and most transactions are due to operations related to the trading of financial products. Consequently, this market is independent of the variations in trade flows. It is more influenced by other macroeconomic variables, such as the growth of GDP, inflation, the mass of interest, and trade balance, among others. The main activity of the FOREX market is currency exchange, and the value of data in the time series (price) is the exchange rate between them. This relationship is called the nominal exchange rate and it indicates the amount of currency the market provides for one monetary unit in exchange for the other. The expression more commonly used to signify the exchange rate between two currencies is the Currency A/Currency B ratio. For instance, USD/EUR represents the exchange value of one US dollar per Euro. The behavior of any type of financial asset, including currencies, has been observed since the creation of this type of market [1; 2; 3; 4]. The way of conducting this study can be classified as fundamental or technical analysis. Fundamental analysis (FA) studies the price of an asset from the point of view of the future evolution of its performance [5]. Technical analysis (TA) [6] is based mainly on a market action study through graphics. For both, it is essential to identify the tendency in the early stages so that the following operations are conducted in the right direction. The final objective of the analysis is to construct trading rules and follow them to operate in the market, enabling exchange operations to obtain economic returns with the minimum risk. TA is based on technical stock market indicators (TIs) configured according to a set of parameters, which work on discrete time series of prices of the target value. There is a wide range of TIs--some simple, others more elaborate. All TA tools intend to obtain relevant information to help investors make sound investment decisions, defining buying and selling operations, even under conditions of uncertainty. There are different ways to evaluate the results obtained by these techniques. One of the most common is to compare the results obtained with the strategy of \"Buy and Hold\" (B&H).This is based on the view that long-term financial markets receive a reasonable rate of return despite periods of volatility or loss. Despite its simplicity, B&H is one of the strategies most used by investors, not only for comparison but also for investing. Several studies have applied Evolutionary Computation techniques to obtain trading rules to maximize the benefits of investments [4; 7; 8; 9; 10]. [PERSON] and [PERSON] conduct a good review of these techniques in [2; 3; 11], where the use of Multi-objective Genetic Programming is suggested to discover effective trading rules. More recently, [PERSON] et al. [12; 13] show that parameter optimization of TIs with Multi-objective Evolutionary algorithms (MOEAs) could generate investment strategies that improve the performance of B&H. In this paper, we expand the above proposals with a new approach that uses different evolutionary algorithms to obtain the best values of the parameters of technical indicators and generate investment rules. The novelty is that we construct the Pareto set of solutions by combining those obtained with different MOEAs. In particular, we apply NGSA-II [14], SPEA-II [15], PAES [16], PESAII [17] and MoCell [18]. These MOEAs are important references in the literature designed to cover several parts of the search space. We propose to make them collaborate in the exploration of the search space.