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Many problems considered in optimization and artificial intelligence research are static: information about the problem is known a priori, and little to no uncertainty about this information is presumed to exist. Most real problems, however, are dynamic: information about the problem is released over time, uncertain events may occur, or the requirements of the problem may change as time passes. One technique for improving optimization and learning in dynamic environments is by using information from the past. By using solutions from previous environments, it is often easier to find promising solutions in a new environment. A common way to maintain and exploit information from the past is the use of memory, where solutions are stored periodically and can be retrieved and refined when the environment changes. Memory can help search respond quickly and efficiently to changes in a dynamic problem. Despite their strengths, standard memories have many weaknesses which limit their effectiveness. This thesis explores ways to improve memory for optimization and learning in dynamic environments. The techniques presented in this thesis improve memories by incorporating probabilistic models of previous solutions into memory, storing many previous solutions in memory while keeping overhead low, building long-term models of the dynamic search space over time, allowing easy refinement of memory entries, and mapping previous solutions to the current environment for problems where solutions may become obsolete over time. To address the weaknesses and limitations of standard memory, two novel classes of memory are introduced: density-estimate memory and classifier-based memory. Density-estimate memory builds and maintains probabilistic models within memory to create density estimations of promising areas of the search space over time.